GECCO 2014 - Learning Classifier System Tutorial

Pier Luca Lanzi - GECCO-2014 July 12-16 2014 Vancouver BC

Learning Classifier SystemsA Gentle Introduction

Outline

bull Introduction Why When What areas What Applications

bull Learning Classifier Systems What Learning Classifiers How do they work What decisions General principles Better classifiers Theory

bull Survey of applications

2

why

4

a real systemwith an unknown

underlying dynamics

Why What was the goal

if C1 buy 30

if C2 sell -2

hellip

evolved rules provide

a plausible humanreadable model of

the unknown system

apply a classifier system online

to generate a behavior matched the real system

To state in concrete technical form a model of a complete mind and its several aspects

5

bull A cognitive system interactingwith an environment

bull Binary detectors and effectors

bull Knowledge = set of classifiers

bull Condition-action rules that recognize a situation and propose an action

bull Payoff reservoir forthe systemrsquos needs

bull Payoff distributed through an epochal algorithm

bull Internal memory as message list

bull Genetic search of classifiers

Hollandrsquos Vision Cognitive System One

6Hollandrsquos Learning Classifier Systems

bull Explicit representation of the incoming reward

bull Good classifiers are the ones that predict high rewards

bull Credit Assignment usingBucket Brigade

bull Rule Discovery througha genetic algorithm applied to the entirerule base (on the whole solution)

bull Description was vastIt did not work right offVery limited success

bull David E Goldberg Computer-aided gas pipeline operation using genetic algorithms and rule learning PhD thesis University of Michigan Ann Arbor MI

Rule Discovery Component

Perceptions

Detectors

Reward Action

Effectors

Match Set

Classifiers matching

the current sensory inputs

Population

Classifiers representing the current knowledge

Evaluation of the actions in the match set

Credit Assignment Component

1 2

3

7Learning System LS-1 amp Pittsburgh Classifier Systems

Holland models learning as ongoing adaptation process

De Jong instead views learning as optimization Genetic algorithms applied to a population of rule

sets1 t = 02 Initialize the population P(t)3 Evaluate the rules sets in P(t)4 While the termination condition is not satisfied5 Begin6 Select the rule sets in P(t) and generate Ps(t)7 Recombine and mutate the rule sets in Ps(t)8 P(t+1) = Ps(t)9 t = t+1 10 Evaluate the rules sets in P(t)11 End No apportionment of credit

Offline evaluation of rule sets

PittsburghClassifier System

when

1970s

1980s

1990s

2000s

XCS is born first results on classificationamp robotics applications but interest fades way

Genetic algorithms and CS-1 Research flourishes success is limited

Evolving rules as optimizationResearch follows Hollandrsquos visionSuccess is still limited

Classifier systems finally workFocus on classification (UCS) Large development of models Facetwise theory and applicationsGenomic and epidemiological applications

Reinforcement Learning

amp Machine Learning

10

Stewart W Wilson amp The XCS Classifier System

1Simplify the model

2Go for accurate predictionsnot high payoffs

3Apply the genetic algorithm to subproblems not to the whole problem

4Focus on classifier systems as reinforcement learning with rule-based generalization

5Use reinforcement learning (Q-learning) to distribute reward

bull Wilson SW Classifier Fitness Based on Accuracy Evolutionary Computation 3(2) 149-175 (1995)

Most developed and studied model so far

for what

Classification(label prediction)

Regression(numerical prediction)

Sequential Decision Making

13

Computational

Models of Cognition

ComplexAdaptiveSystems

Classificationamp Data mining

AutonomousRobotics

OthersTraffic controllersTarget recognition

Fighter maneuveringhellip

learning classifier systems

15

>

16

bull The goal is to maximize the amount of reward received

bull How much future reward when at is performed in st

bull What is the expected payoff for st and at

bull Need to compute a value function Q(stat) payoff

Learning Classifier Systems asReinforcement Learning Methods

Environment

Agent

st atrt+1st+1

How does reinforcement learning work

Define the inputs the actions and how the reward is determined

Define the expected payoff

Compute a value function Q(stat) mapping state-action pairs into expected payoffs

18

bull At the beginning is initialized with random values

bull At time t

bull Parameters Discount factor The learning rate The action selection strategy

How does reinforcement learning work Then Q-learning is an option

incoming rewardnew estimate

previous value

new estimate

Reinforcement learning assumes that Q(stat) is represented as a table

But the real world is complex the number of possible inputs can be huge

We cannot afford an exact Q(stat)

20

The Mountain Car Example

GOAL

Task drive an underpowered car up a steep mountain road

a t =

acc

lef

t a

cc

righ

t n

o ac

c

st = position velocity

rt = 0 when goal is reached -1 otherwise

Value Function Q(stat)

21

What are the issues

bullExact representation infeasible

bullApproximation mandatory

bullThe function is unknown it is learnt online from experience

Learning an unknown payoff functionwhile also trying to approximate it

Approximator works on intermediate estimatesWhile also providing information for the learning

Convergence is not guaranteed

Whats does this have to do with Learning Classifier Systems

They solve reinforcement learning problems

Represent the payoff function Q(st at) as a population of rules the classifiers

Classifiers are evolved while Q(st at) is learned online

classifiers

24

payoff

surface for A

What is a classifier

IF condition C is true for input s THEN the payoff of action A is p

s

payoff

l u

p

ConditionC(s)=llesleu

General conditions covering large portions of

the problem space

Accurate approximations

25

What types of solutions

how do they work

bull Two key components

bull A genetic algorithm works on problem space decomposition (condition-action)

bull Supervised or reinforcement learning is used for learning local prediction models

Problem Space

28

How do learning classifier systems workThe main performance cycle

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

Rules describing the current solution

30

state st

Matching

EnvironmentAgent

Population [P]

Rules whose condition match st

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

The value of each action in [M]

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

Rules in [M] with the selected action

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

The classifiers predict an expected payoff

The incoming reward is used to updatethe rules which helped in getting the reward

Any reinforcement learning algorithm can be used to estimate the classifier prediction

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

Action Set at t-1 [A]-1

ReinforcementLearning

36

How do learning classifier systems workThe reinforcement componentbull For each classifier C in [A]-1 the prediction p is updated as

follows

P r + maxaA PredictionArray(a)

p p + (P- p)

bull Compare this with Q-learning

A rule ldquocorrespondsrdquo to Q-tablep ldquocorrespondsrdquo to the value Q(sa)

P ldquocorrespondsrdquo to ldquor+maxaQ(sta)rdquo

Where do classifiers come from

In principle any search method may be used

Evolutionary computation is nice becauseit is representation ldquoindependentrdquo

A genetic algorithm select recombines mutate existing classifiers to search for

better ones

What are the good classifiersWhat is the classifier fitness

The goal is to approximate a target value function

with as few classifiers as possible

We wish to have an accurate approximation

One possible approach is to define fitness as a function of the classifier prediction

accuracy

What about generalization

The genetic algorithm can take care of this

General classifiers apply more oftenthus they are reproduced more

But since fitness is based on classifiers accuracy

only accurate classifiers are likely to be reproduced

The genetic algorithm evolves maximally general maximally accurate

classifiers

what decisions

41

How to apply learning classifier systems

bull Determine the inputs the actions and how reward is distributed

bull Determine what is the expected payoffthat must be maximized

bull Decide an action selection strategybull Set up the parameter

Environment

Learning Classifier System

st rt at

bull Select a representation for conditions the recombination and the mutation operators

bull Select a reinforcement learning algorithm

bull Setup the parameters mainly the population size the parameters for the genetic algorithm etc

bull Parameter

42

Things can be extremely simpleFor instance in supervised classification

Environment

example class1 if the class is correct

0 if the class is not correct

bull Select a representation for conditions and the recombination and mutation operators

general principles

An Examplehellip 44

A1 A2 A3 A4 A5 A6 CLASS1 1 1 1 3 1 11 1 1 1 3 2 11 1 1 3 2 1 11 1 1 3 3 2 11 1 2 3 1 2 11 2 1 1 1 2 11 2 1 1 2 1 01 2 1 1 3 1 01 2 1 1 4 2 01 2 1 2 1 1 11 2 1 2 3 1 0hellip hellip hellip hellip hellip hellip hellip

Six Attributes

Severa

l ca

ses

A hidden concepthellip

What is the concept

Traditional Approach

bull Classification Trees C45 ID3 CHAID hellip

bull Classification Rules CN2 C45rules hellip

bull Prediction Trees CART hellip

45

Task

Representation

Algorithm

I Need to Classify I Want Trees What Algorithm ID3 C45 CHAID

46

I Need to Classify I Want Rules What Algorithm

bull OneRuleif A5 = 3 then 0 (1119)if A5 = 2 then 0 (1120)if A5 = 4 then 0 (1123)if A5 = 1 then 1 (290)

correct 91 out of 124 training examples

bull Rule Learnerif A5 = 4 and A1 = 1 then 0 (113)if A5 = 1 then 1 (290)if A4 = 2 and A5 = 2 then 0 (16)if A1 = 1 and A2 = 2 then 0 (010)else 0 (2729)

47

FOILis_0(A1A2A3A4A5A6) - a1 a2 and a5 1

Different task different solution representationCompletely different algorithm

Thou shalt have no other model

Genetics-Based Generalization

Accurate EstimatesAbout Classifiers

(Powerful RL)

ClassifierRepresentation

50

Learning Classifier SystemsOne Principle Many Representations

GeneticSearch

EstimatesRL amp MLKnowledge

RepresentationConditions amp

Prediction

Ternary Conditions0 1

SymbolicConditions

Attribute-ValueConditions

Ternary rules0 1

if a5lt2 or

a1=a2 class=1 if A1=1and A2=11if A1=2 andA2=21

Attribute-ValueConditionsSymbolic

Conditions

Same frameworkJust plug-in your favorite representation

better classifiers

52

payoff

landscape of A

What is computed prediction

Replace the prediction p by a parametrized function p(sw)

s

payoff

l u

p(sw)=w0+sw1

Which Representation

Which type of approximation

Stewart W Wilson Classifiers that approximate functions Natural Computing 1(2-3) 211-234 (2002)

53

Same example with computed prediction

No need to change the framework

Just plug-in your favorite estimator

Linear Polynomial NNs SVMs tile-coding

Lanzi PL Extending XCSF beyond linear approximation GECCO 2005Loiacono D and Lanzi PL Evolving neural networks for classifier prediction with XSCF ECAI 2006 Lanzi and Loiacono XCSF with neural prediction IEEE CEC 2006Loiacono D Marelli A and Lanzi PL Support vector regression for classifier prediction GECCO 2007

What do we want

Fast learningLearn something as soon as possible

Accurate solutionsAs the learning proceeds

the solution accuracy should improve

Is there another approach

payoff

landscape

s

payoff

l u

p(sw)=w0

p(sw)=w1s+w0p(sw)=NN(sw)

Initially constant prediction may be

good

As learn proceeds the solution should

improvehellip

improvehelliphellip as much as possiblehellip as much as possible

55

Ensemble Classifiers 56

None of prediction models is the ldquobestrdquo Let evolution search for the ldquomost suitedrdquo

NNNN

Almost as fast as using best model Model is adapted effectively in each subspace

any theory

Learning Classifier Systems

Representation Reinforcement Learningamp Genetics-based Search

Unified theory is impractical

Develop facetwise models

59

Facetwise Models for a Theory of Evolution and Learning

bull Prof David E GoldbergUniversity of Illinois at Urbana Champaign

bull Facetwise approach for the analysis and the design of genetic algorithms

bull In learning classifier systemsSeparate learning from evolutionSimplify the problem by focusing

only on relevant aspectDerive facetwise models

bull Applied to model several aspects of evolution

provaf (x)prova

S([A]) = S([P])(2-S([P]))Since the genetic algorithm is applied to the action set

there is a generalization pressure regulated by this equation

Ngt-log(1-θ)pGiven the probability θ to maintain all the subsolutions

with occurrence probability p then the population size N hellip

O(L 2o+a)Time to converge for a problem of L bits order o

and with a problem classes

Martin V Butz Tim Kovacs Pier Luca Lanzi Stewart W Wilson Toward a theory of generalization andlearning in XCS IEEE Trans Evolutionary Computation 8(1) 28-46 (2004)

Martin V Butz Kumara Sastry David E Goldberg Strong Stable and Reliable Fitness Pressure in XCS due to Tournament Selection Genetic Programming and Evolvable Machines 6(1) 53-77 (2005)

Martin V Butz David E Goldberg Pier Luca Lanzi Bounding Learning Time in XCS GECCO (2) 2004 739-750

advanced topicshellip

What the Advanced Topics

bull Models Tailored for ApplicationsAnticipatory Behavior (ACS)Data Mining classification and prediction (eg

UCS)Epidemiology (eg EpiCS EpiXCS)ExSTraCS (Bioinformatics)

bull Improved representations of conditions (GP GEP hellip)

bull Improved representations of actions (GP Code Fragments)

bull Improved genetic search (EDAs ECGA BOA hellip)

bull Improved estimators

bull ScalabilityMatchingDistributed models

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

What ApplicationsComputational Models of Cognition

bull Learning classifier system model certain aspects of cognitionHuman language learningPerceptual category learningAffect theoryAnticipatory and latent learning

bull Learning classifier systems provide good models for animals in experiments in which the subjects must learn internal models to perform as well as they do

bull Martin V Butz University of WuumlrzburgDepartment of Cognitive Psychology III Cognitive Bodyspaces Learning and Behavior (COBOSLAB)

bull Wolfgang Stolzmann Daimler Chrysler bull Rick R Riolo University of Michigan

Center for the Study of Complex Systems

67

References

bull Butz MV Anticipatory Learning Classifier Systems Genetic Algorithms and Evolutionary Computation vol 4 Springer-Verlag (2000)

bull Riolo RL Lookahead Planning and Latent Learning in a Classifier System In JA Meyer SW Wilson (eds) From Animals to Animats 1 Proceedings of the First International Conferenceon Simulation of Adaptive Behavior (SAB90) pp 316326 A Bradford Book MIT Press (1990)

bull Stolzmann W and Butz MV and Hoffman J and Goldberg DE First Cognitive Capabilities in the Anticipatory Classifier System In From Animals to Animats Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior MIT Press (2000)

computational economics

69

What ApplicationsComputational Economics

bull To models one single agent acting in the market (BW Arthur JH Holland B LeBaron)

bull To model many interactive agents each onecontrolled by its own classifier system

bull Modeling the behavior of agents trading risk free bonds and risky assets

bull Different trader types modeled by supplying different input information sets to a group of homogenous agents

bull Later extended to a multi-LCS architecture applied to portfolio optimization

bull Technology startup company founded in March 2005

70

References

bull Sor Ying (Byron) Wong Sonia Schulenburg Portfolio allocation using XCS experts in technical analysis market conditions and options market GECCO (Companion) 2007 2965-2972

bull Sonia Schulenburg Peter Ross An Adaptive Agent Based Economic Model Learning Classifier Systems 1999 263-282

bull BW Arthur JH Holland B LeBaron R Palmer and P Tayler Asset Pricing Under Endogenous Expectations in an Artificial Stock Marketldquo in The Economy as an Evolving Complex System II Edited (with S Durlauf and D Lane) Addison-Wesley 1997

bull BW Arthur R Palmer J Holland B LeBaron and P Taylor Artificial Economic Life a Simple Model of a Stockmarketldquo Physica D 75 264-274 1994

data analysis

72

What ApplicationsClassification and Data Mining

bull Bull L (ed) Applications of LearningClassifier Systems Springer (2004)

bull Bull L Bernado Mansilla E amp Holmes J (eds) Learning Classifier Systems in Data Mining Springer (2008)

bull Nowadays by far the most important application domain for LCSs

bull Many models GA-Miner REGAL GALE GAssist

bull Performance comparable to state of the art machine learning

Human Competitive Results 2007X Lloragrave R Reddy B Matesic R Bhargava Towards Better

than Human Capability in Diagnosing Prostate Cancer Using Infrared Spectroscopic Imaging

hyper heuristics

74

What ApplicationsHyper-Heuristics

bull Ross P Marin-Blazquez J Schulenburg S and Hart E Learning a Procedure that can Solve Hard Bin-packing Problems A New GA-Based Approach to Hyper-Heuristics In Proceedings of GECCO 2003

bull Bin-packing and timetabling problems

bull Pick a set of non-evolutionary heuristics

bull Use classifier system to learn a solution process not a solution

bull The classifier system learns a sequence of heuristics which should be applied to gradually transform the problem from its initial state to its final solved state

medical data

76

What ApplicationsEpidemiologic Surveillance

bull John H HolmesCenter for Clinical Epidemiology amp BiostatisticsDepartment of Biostatistics amp EpidemiologyUniversity of Pennsylvania - School of Medicine

bull Epidemiologic surveillance data need adaptivity to abrupt changes

bull Readable rules are attractive

bull Performance similar to state of the art machine learning

bull But several important feature-outcome relationships missed by other methods were discovered

bull Similar results were reported by Stewart Wilson for breast cancer data

77

References

bull John H Holmes Jennifer A Sager Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS An Evolutionary Computation Approach AIME 2005 444-452

bull John H Holmes Dennis R Durbin Flaura K Winston A New Bootstrapping Method to Improve Classification Performance in Learning Classifier Systems PPSN 2000 745-754

bull John H Holmes Dennis R Durbin Flaura K Winston The learning classifier system an evolutionary computation approach to knowledge discovery in epidemiologic surveillance Artificial Intelligence in Medicine 19(1) 53-74 (2000)

autonomous robotics

79

What ApplicationsAutonomous Robotics

bull In the 1990s a major testbed for learning classifier systems

bull Marco Dorigo and Marco Colombetti Robot Shaping An Experiment in Behavior Engineering 1997

bull They introduced the concept of robot shaping defined as the incremental training of an autonomous agent

bull Behavior engineering methodology named BAT Behavior Analysis and Training

bull University of West England applied several learning classifier system models to several robotics problems

artificial ecosystems

81

What ApplicationsModeling Artificial Ecosystems

bull Jon McCormack Monash University

bull Eden an interactive self-generating artificial ecosystem

bull World populated by collections of evolving virtual creatures

bull Creatures move about the environment Make and listen to sounds Foraging for food Encountering predators Mating with each other

bull Creatures evolve to fit their landscape

bull Eden has four seasons per year (15mins)

bull Simple physics for rocks biomass and sonic animals Jon McCormack

82

Eden An Evolutionary Sonic Ecosystem

83

References

bull McCormack J Impossible Nature The Art of Jon McCormack Published by the Australian Centre for the Moving ImageISBN 1 920805 08 7 ISBN 1 920805 09 5 (DVD)

bull J McCormack New Challenges for Evolutionary Music and Art ACM SIGEVOlution Newsletter Vol 1(1) April 2006 pp 5-11

bull McCormack J 2005 On the Evolution of Sonic Ecosystems in Adamatzky et al (eds) Artificial Life Models in Software Springer Berlin

bull McCormack J 2003 Evolving Sonic Ecosystems Kybernetes 32(12) pp 184-202

chemical amp neuronal networks

85

What ApplicationsChemical and Neuronal Networks

bull L Bull A Budd C Stone I Uroukov B De Lacy Costello and A AdamatzkyUniversity of the West of England

bull Behaviour of non-linear media controlled automatically through evolutionary learning

bull Unconventional computing realised by such an approach

bull Learning classifier systemsControl a light-sensitive sub-excitable

Belousov-Zhabotinski reactionControl the electrical stimulation of

cultured neuronal networks

86

bull To control a light-sensitive sub-excitable BZ reaction pulses of wave fragments are injected into the checkerboard grid resulting in rich spatio-temporal behaviour

bull Learning classifier system can direct the fragments to an arbitrary position through control of the light intensity within each cell

bull Learning Classifier Systems control the electrical stimulation of cultured neuronal networks such that they display elementary learning respond to a given input signal in a pre-specified way

bull Results indicate that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks

87

References

bull Larry Bull Adam Budd Christopher Stone Ivan Uroukov Ben De Lacy Costello and Andrew Adamatzky Towards Unconventional Computing through Simulated Evolution Learning Classifier System Control of Non-Linear MediaArtificial Life (to appear)

bull Budd A Stone C Masere J Adamatzky A DeLacyCostello B Bull L Towards machine learning control of chemical computers In A Adamatzky C Teuscher (eds) From Utopian to Genuine Unconventional Computers pp 17-36 Luniver Press

bull Bull L Uroukov IS Initial results from the use of learning classier systems to control n vitro neuronal networks In Lipson [189] pp 369-376

conclusions

89

Conclusions

bull Cognitive Modeling

bull Complex Adaptive Systems

bull Machine Learning

bull Reinforcement Learning

bull Metaheuristics

bull hellip

Many blocks to plug-in Several representations Several RL algorithms Several evolutionary methods hellip

Additional Information

bull WWW httpgbmlorg httpwwwilligalorg UWE Learning Classifier Systems Group

httpwwwcemsuweacuklcsg A Learning Classifier Systems Bibliography

httpwwwcsbrisacuk~kovacslcssearchhtml

bull Mailing lists lcs-and-gbml group Yahoo

bull Proceedings of the International Workshop on Learning Classifier Systems (Lanzi Stolzmann amp Wilson 2000 2001 2002 Kovacs Lloragrave amp Takadama 2003-2005 Bacardit Bernadoacute-Mansilla Butz Kovacs Lloragrave Takadama IWLCS2007)

bull IWLCS here (too bad if you did not come)

90

Books

bull Bull L (Ed) Applications of learning classifier systems Berlin Heidelberg Springer-Verlag

bull Butz M V (2002) Anticipatory learning classifier systems Kluwer Academic Publishers Boston MA

bull Butz M V (2006) Rule-based evolutionary online learning systems A principled approach to LCS analysis and design Studies in Fuzziness and Soft Computing Series Springer Verlag Berlin Heidelberg Germany

bull Bull L amp Kovacs T (Eds) (2005) Foundations of learning classifier systems Berlin Heidelberg Springer-Verlag

bull Lanzi P L Stolzmann W amp Wilson S W (Eds) (2000) Learning classifier systems From foundations to applications (LNAI 1813) Berlin Heidelberg Springer-Verlag

bull Drugowitsch J (2008) Design and Analysis of Learning Classifier Systems A Probabilistic Approach Springer-Verlag

bull Goldberg D E (1989) Genetic algorithms in search optimization amp machine learning Addison-Wesley

bull Holland JH (1975) Adaptation in natural and artificial systems University of Michigan Press

bull Kovacs T (2004) Strength of accuracy Credit assignment in learning classifier systems Berlin Heidelberg Springer-Verlag

91

Software

bull httpwwwilligalorgMartin Butzrsquos ACS amp XCS in C and JavaPier Luca Lanzirsquos C++ XCSLib (XCS and XCSF)

bull httpmedalcsumsledufilesXCSFJava11zipMartin Butzrsquos XCSF in Java

bull httpwwwryanurbanowiczcomRyanrsquos Urbanowiczrsquos ExSTraCS

bull Educational LCS (eLCS) httpsourceforgenetprojectseducationallcs Includes a basic guide for each implementation which

progressively adds major components of a Michigan-Style LCS algorithm

Code intended to be paired with the first LCS introductory textbook written by Will Browne

92

Thank youQuestions

Slide 1
Outline
Slide 3
Hollandrsquos Learning Classifier Systems
Learning System LS-1 amp Pittsburgh Classifier Systems
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Learning Classifier Systems as Reinforcement Learning Methods
Slide 17
How does reinforcement learning work Then Q-learning is an o
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
How do learning classifier systems work The main performance c
How do learning classifier systems work The main performance c (2)
How do learning classifier systems work The reinforcement comp
Slide 37
Slide 38
Slide 39
Slide 40
Things can be extremely simple For instance in supervised clas
Slide 43
An Examplehellip
I Need to Classify I Want Trees What Algorithm ID3 C45 CH
Slide 48
Slide 49
Learning Classifier Systems One Principle Many Representations
Slide 51
Slide 54
Ensemble Classifiers
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
What Applications Computational Models of Cognition
References
Slide 68
What Applications Computational Economics
References (2)
Slide 71
What Applications Classification and Data Mining
Slide 73
What Applications Hyper-Heuristics
Slide 75
What Applications Epidemiologic Surveillance
References (3)
Slide 78
What Applications Autonomous Robotics
Slide 80
What Applications Modeling Artificial Ecosystems
References (4)
Slide 84
What Applications Chemical and Neuronal Networks
What Applications Chemical and Neuronal Networks (2)
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 2: GECCO 2014 - Learning Classifier System Tutorial

Outline

bull Introduction Why When What areas What Applications

bull Learning Classifier Systems What Learning Classifiers How do they work What decisions General principles Better classifiers Theory

bull Survey of applications

2

why

4

underlying dynamics

if C1 buy 30

if C2 sell -2

hellip

the unknown system

5

Perceptions

Detectors

Reward Action

Effectors

Match Set

Population

1 2

3

when

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 3: GECCO 2014 - Learning Classifier System Tutorial

why

4

underlying dynamics

if C1 buy 30

if C2 sell -2

hellip

the unknown system

5

Perceptions

Detectors

Reward Action

Effectors

Match Set

Population

1 2

3

when

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 4: GECCO 2014 - Learning Classifier System Tutorial

4

underlying dynamics

if C1 buy 30

if C2 sell -2

hellip

the unknown system

5

Perceptions

Detectors

Reward Action

Effectors

Match Set

Population

1 2

3

when

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 5: GECCO 2014 - Learning Classifier System Tutorial

5

Perceptions

Detectors

Reward Action

Effectors

Match Set

Population

1 2

3

when

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 6: GECCO 2014 - Learning Classifier System Tutorial

Perceptions

Detectors

Reward Action

Effectors

Match Set

Population

1 2

3

when

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 7: GECCO 2014 - Learning Classifier System Tutorial

when

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 8: GECCO 2014 - Learning Classifier System Tutorial

when

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 9: GECCO 2014 - Learning Classifier System Tutorial

1970s

1980s

1990s

2000s

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 10: GECCO 2014 - Learning Classifier System Tutorial

10

1Simplify the model

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 11: GECCO 2014 - Learning Classifier System Tutorial

for what

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 12: GECCO 2014 - Learning Classifier System Tutorial

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 13: GECCO 2014 - Learning Classifier System Tutorial

13

Computational

Models of Cognition

AutonomousRobotics

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 14: GECCO 2014 - Learning Classifier System Tutorial

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 15: GECCO 2014 - Learning Classifier System Tutorial

15

>

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 16: GECCO 2014 - Learning Classifier System Tutorial

16

Environment

Agent

st atrt+1st+1

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 17: GECCO 2014 - Learning Classifier System Tutorial

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 18: GECCO 2014 - Learning Classifier System Tutorial

18

bull At time t

previous value

new estimate

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 19: GECCO 2014 - Learning Classifier System Tutorial

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 20: GECCO 2014 - Learning Classifier System Tutorial

20

GOAL

a t =

acc

lef

t a

cc

righ

t n

o ac

c

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 21: GECCO 2014 - Learning Classifier System Tutorial

21

What are the issues

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

classifiers

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 24: GECCO 2014 - Learning Classifier System Tutorial

24

payoff

surface for A

s

payoff

l u

p

the problem space

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 25: GECCO 2014 - Learning Classifier System Tutorial

25

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 26: GECCO 2014 - Learning Classifier System Tutorial

how do they work

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 27: GECCO 2014 - Learning Classifier System Tutorial

Problem Space

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 28: GECCO 2014 - Learning Classifier System Tutorial

28

state st

EnvironmentAgent

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 29: GECCO 2014 - Learning Classifier System Tutorial

29

state st

EnvironmentAgent

Population [P]

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 30: GECCO 2014 - Learning Classifier System Tutorial

30

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 31: GECCO 2014 - Learning Classifier System Tutorial

31

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 32: GECCO 2014 - Learning Classifier System Tutorial

32

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 33: GECCO 2014 - Learning Classifier System Tutorial

33

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

EnvironmentAgent

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 34: GECCO 2014 - Learning Classifier System Tutorial

34

state st

Matching

EnvironmentAgent

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action at

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 35: GECCO 2014 - Learning Classifier System Tutorial

35

state st

Matching

Population [P]

Match Set [M]

Action Evaluation

Prediction Array

Action Selection

Action Set [A]

action atreward rt

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 36: GECCO 2014 - Learning Classifier System Tutorial

36

follows

p p + (P- p)

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 37: GECCO 2014 - Learning Classifier System Tutorial

better ones

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

accuracy

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

classifiers

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 40: GECCO 2014 - Learning Classifier System Tutorial

what decisions

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 41: GECCO 2014 - Learning Classifier System Tutorial

41

Environment

st rt at

bull Parameter

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 42: GECCO 2014 - Learning Classifier System Tutorial

42

Environment

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 43: GECCO 2014 - Learning Classifier System Tutorial

general principles

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 44: GECCO 2014 - Learning Classifier System Tutorial

An Examplehellip 44

Six Attributes

Severa

l ca

ses

What is the concept

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 45: GECCO 2014 - Learning Classifier System Tutorial

45

Task

Representation

Algorithm

46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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46

47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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47

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 48: GECCO 2014 - Learning Classifier System Tutorial

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 49: GECCO 2014 - Learning Classifier System Tutorial

(Powerful RL)

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 50: GECCO 2014 - Learning Classifier System Tutorial

50

GeneticSearch

Prediction

SymbolicConditions

Ternary rules0 1

if a5lt2 or

Conditions

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 51: GECCO 2014 - Learning Classifier System Tutorial

better classifiers

52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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52

payoff

landscape of A

s

payoff

l u

p(sw)=w0+sw1

53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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53

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 54: GECCO 2014 - Learning Classifier System Tutorial

What do we want

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 55: GECCO 2014 - Learning Classifier System Tutorial

payoff

landscape

s

payoff

l u

p(sw)=w0

good

improvehellip

55

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 56: GECCO 2014 - Learning Classifier System Tutorial

NNNN

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 57: GECCO 2014 - Learning Classifier System Tutorial

any theory

59

provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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provaf (x)prova

62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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62

what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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what applications

64

Computational

Models of Cognition

AutonomousRobotics

modeling cognition

66

67

References

69

70

References

data analysis

72

hyper heuristics

74

medical data

76

77

References

autonomous robotics

79

81

82

83

References

85

86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
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An Examplehellip
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Books
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bull Metaheuristics

bull hellip

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Books

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92

Thank youQuestions

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An Examplehellip
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Models of Cognition

AutonomousRobotics

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data analysis

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medical data

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autonomous robotics

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conclusions

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bull Metaheuristics

bull hellip

90

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92

Thank youQuestions

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An Examplehellip
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autonomous robotics

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conclusions

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Conclusions

bull Metaheuristics

bull hellip

90

Books

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92

Thank youQuestions

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An Examplehellip
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References (3)
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References (4)
Slide 84
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autonomous robotics

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conclusions

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bull Metaheuristics

bull hellip

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92

Thank youQuestions

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What are the issues
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An Examplehellip
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References (3)
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References (4)
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autonomous robotics

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conclusions

89

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bull Metaheuristics

bull hellip

90

Books

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92

Thank youQuestions

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An Examplehellip
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References (4)
Slide 84
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conclusions

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Conclusions

bull Metaheuristics

bull hellip

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92

Thank youQuestions

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An Examplehellip
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References (3)
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References (4)
Slide 84
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autonomous robotics

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Conclusions

bull Metaheuristics

bull hellip

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Books

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92

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An Examplehellip
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References
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References (4)
Slide 84
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conclusions

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Conclusions

bull Metaheuristics

bull hellip

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Books

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92

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An Examplehellip
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References
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References (3)
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References (4)
Slide 84
References
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Conclusions
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conclusions

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Conclusions

bull Metaheuristics

bull hellip

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Thank youQuestions

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Outline
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What are the issues
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An Examplehellip
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References
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References (2)
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References (3)
Slide 78
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References (4)
Slide 84
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conclusions

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bull Metaheuristics

bull hellip

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Books

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Thank youQuestions

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Outline
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What are the issues
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An Examplehellip
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References
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References (2)
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References (3)
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References (4)
Slide 84
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Slide 88
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Conclusions

bull Metaheuristics

bull hellip

90

Books

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92

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What are the issues
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An Examplehellip
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References
Slide 68
References (2)
Slide 71
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References (3)
Slide 78
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References (4)
Slide 84
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Slide 88
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bull hellip

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What are the issues
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An Examplehellip
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References
Slide 68
References (2)
Slide 71
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References (3)
Slide 78
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References (4)
Slide 84
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Slide 88
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bull hellip

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What are the issues
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An Examplehellip
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References
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References (3)
Slide 78
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References (4)
Slide 84
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bull hellip

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An Examplehellip
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References
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References (3)
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References (4)
Slide 84
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An Examplehellip
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References
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References (4)
Slide 84
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bull hellip

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An Examplehellip
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References
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References (3)
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References (4)
Slide 84
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An Examplehellip
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References
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References (3)
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References (4)
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An Examplehellip
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References
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References (4)
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An Examplehellip
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References
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References (4)
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An Examplehellip
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References (4)
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An Examplehellip
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An Examplehellip
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References
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Slide 19
What are the issues
Slide 22
Slide 23
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Slide 27
Slide 37
Slide 38
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Slide 43
An Examplehellip
Slide 48
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Slide 57
Slide 58
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Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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86

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
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Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 87: GECCO 2014 - Learning Classifier System Tutorial

87

References

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 88: GECCO 2014 - Learning Classifier System Tutorial

conclusions

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 89: GECCO 2014 - Learning Classifier System Tutorial

89

Conclusions

bull Metaheuristics

bull hellip

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 90: GECCO 2014 - Learning Classifier System Tutorial

90

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 91: GECCO 2014 - Learning Classifier System Tutorial

Books

91

Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

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Software

92

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Page 93: GECCO 2014 - Learning Classifier System Tutorial

Thank youQuestions

Slide 1
Outline
Slide 3
Slide 8
Slide 9
Slide 11
Slide 12
Slide 13
Slide 14
Slide 15
Slide 17
Slide 19
What are the issues
Slide 22
Slide 23
Slide 26
Slide 27
Slide 37
Slide 38
Slide 39
Slide 40
Slide 43
An Examplehellip
Slide 48
Slide 49
Slide 51
Slide 54
Slide 57
Slide 58
Slide 60
Slide 61
Slide 63
Slide 64
Slide 65
References
Slide 68
References (2)
Slide 71
Slide 73
Slide 75
References (3)
Slide 78
Slide 80
References (4)
Slide 84
References
Slide 88
Conclusions
Books
Software
Slide 93

Date post:	20-Aug-2015
Category:	Science
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GECCO 2014 - Learning Classifier System Tutorial

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