Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Analysing the Performance of DifferentPopulation Structures for an Agent-based

Evolutionary Algorithm

Juan Luis Jimenez Laredo et al.

Dpto. Arquitectura y Tecnologıa de ComputadoresUniversidad de Granada

18-Jan-2011

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Scope

• Status: Peer-to-Peer Evolutionary Computation (P2P EC)represents a parallel solution for hard problemsoptimization

• Modelling: Fine grained parallel EA using a P2P protocolas underlying population structure

• Objective: Comparison of different population structureson the EA performance

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Outline

1 IntroductionP2P in a NutshellThe Evolvable Agent

2 Experimental AnalysisGoalsMethodologyAnalysis of Results

3 Conclusions

4 Future Works

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Outline

1 IntroductionP2P in a NutshellThe Evolvable Agent

2 Experimental AnalysisGoalsMethodologyAnalysis of Results

3 Conclusions

4 Future Works

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

P2P in a Nutshell

P2P EC

• Virtualization:Single view atapplication level

• Decentralization:No centralmanagement

• Massive Scalability:Up to thousands ofcomputers

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Outline

1 IntroductionP2P in a NutshellThe Evolvable Agent

2 Experimental AnalysisGoalsMethodologyAnalysis of Results

3 Conclusions

4 Future Works

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

The Evolvable Agent Model

Design principles• Agent based approach

• Fine grain parallelization

• Spatially structured EA

• Local selection

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

The Evolvable Agent Model

Design principles• Agent based approach

• Fine grain parallelization

• Spatially structured EA

• Local selection

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Outline

1 IntroductionP2P in a NutshellThe Evolvable Agent

2 Experimental AnalysisGoalsMethodologyAnalysis of Results

3 Conclusions

4 Future Works

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Goals and Test-Cases

Goal

• Comparison of performances using different populationstructures

Ring Watts-Strogatz Newscast

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Outline

1 IntroductionP2P in a NutshellThe Evolvable Agent

2 Experimental AnalysisGoalsMethodologyAnalysis of Results

3 Conclusions

4 Future Works

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Experimental settings

• 2-Trap. L=12...60

• Population size• Estimated by bisection• Selectorecombinative

GA (Mutation less)• Minimum population

size able to reach 0.98of SR

• Uniform Crossover

• Binary Tournament

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Outline

1 IntroductionP2P in a NutshellThe Evolvable Agent

2 Experimental AnalysisGoalsMethodologyAnalysis of Results

3 Conclusions

4 Future Works

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Population Structure

Settings

Problem instance: 2-trapPop. Size: Tuning AlgorithmNo Mutation

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Population Structure

Settings

Problem instance: L=60 2-trapPop. Size: 135Max. Eval: 5535Mutation: Bit-flip Pm = 1

L

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Conclusions

• Regular lattices require of smaller population sizes... BUT a bigger number of evaluations to find a solution.

• Different small-world methods produce an equivalentperformance...That’s good! Many P2P protocol are designed to workas small-world networks(i.e. Interoperability/Migration between P2P platforms)

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Future Works

• Validation of the model in a real P2P infrastructure

• Exploration of other P2P protocols as populationstructures

• Extension of the P2P concept to other metaheuristics

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Introduction

P2P in aNutshell

The EvolvableAgent

ExperimentalAnalysis

Goals

Methodology

Analysis ofResults

Conclusions

Future Works

Questions

Thanks for your attention!

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