Evolutionary Fuzzy Logic Computation -...

Chapter 1: Introduction

1

A Genetic-Fuzzy Approach to Measure Multiple Intelligence


Evolutionary Computation

• Soft Computing

• Genetic Algorithm

• Fuzzy Logic

Application

Domain

• Theory of MultipIe Intelligence

Intelligent System

Framework

• Objectives of Research

• Flow of Thesis


2


1.1 Chapter Overview

The chapter briefly discusses computing methods such as hard computing and soft

computing by highlighting advantages of both the techniques along with their detailed

classification. It justifies the needs of soft computing methods for designing

intelligent systems. Here, the classification taxonomy of search and optimization

process is presented. The chapter further explains the role of soft computing for

search and optimization in detail. The chapter highlights prime soft computing

constituents. It also justifies the importance of evolutionary computing. The role of

Genetic Algorithm is illustrated along with its advantages. The chapter explains the

need of hybridization of Genetic Algorithm and Fuzzy Logic. Here, educational

perspective of the Theory of Multiple Intelligence has been explained in order to

design the research problem. The need of research work has been discussed in detail.

The chapter narrates the objectives of research work along with the way to achieve the

stated objectives. The chapter discusses the main challenges of the research work. The

prime challenge is to design generic framework for developing intelligent decision

support system based on Genetic-Fuzzy hybridization. At the end, the chapter

concludes with organization of thesis describing brief introduction of every chapter.

1.2 Hard Computing and Soft Computing

Hard computing is basically conventional computing. The term hard computing was

first invented by Prof. L.A. Zadeh of the University of California, USA, in 1996

[125], although it had been in use to solve different problems for a long time [45, p.1].

Hard computing techniques are traditional computing techniques based on principles

of precision, certainty and inflexibility. Real world problems which deal with

changing of information and imprecise behavior cannot be handled by such

techniques. This type of computing is capable of solving problems which requires a

mathematical model. It has been noticed that hard computing techniques may be

efficient to solve real life problems. However, the major limitation of hard computing

technique is that it consumes a lot of computation time to deal with real life problems

as real life problems are designed to handle imprecise and uncertain information.

There are various analytical models available for handling pre-determined

requirements of real life problems. However, real scenario shows that the real world

problems exist in a non-ideal environment. Many contemporary problems do not


3


accommodate hard computing techniques for precise solutions; major of them are

enlisted as follows [187]:

Recognition problems e.g. handwriting, speech, objects, images;

Mobile robot co-ordination, forecasting;

Combinatorial problems.

According to Akerakar & Sajja [174, p.239], hard computing techniques deal with

precise, complete and full - truth- based system. Hence, there is always a requirement

of computational methods which can be suitable to handle problems which are

difficult to be modeled in a pre-defined manner. Figure 1.1 outlines the major

computational paradigms as shown below.

Figure 1.1: Classification of Computational Approaches

Computational paradigms are mainly classified as hard and soft computing. Hard

computing methods deal with precise models while soft computing (SC) methods deal

with approximate models. Precise models are classified into two sub branches namely

symbolic logic and reasoning as well as traditional search and numerical search

methods. These methods utilize the basics of traditional artificial intelligence. There

can be several other sub branches for each of the methods.

Computational Paradigms

Hard Computing

Precise Models

Symbolic Logic and Reasoning

Traditional Numerical odeling

& Search

Soft Computing

Imprecise Models

Approximate Reasoning

Functional Optimization & Random Search


4


Soft computing deals with approximate models. Approximate models are branched

into approximate reasoning and functional optimization as well as random search

method. The term soft computing was introduced by Prof. Zadeh in (1992) [123,

pp.13-14]. In effect, the role model for soft computing is the human mind [124].

According to Zadeh [126, p.1],

The guiding principle of soft computing is as follows:

“To exploit the tolerance for imprecision, uncertainty, partial truth, and approximation

to achieve robustness, low solution cost and better rapport with reality.”

Soft computing is viewed as a foundation component for the emerging field of

computational intelligence [93]. It can be used to address a very wide range of

problems in all industries and business sectors. In general, soft computing is a good

option for complex systems [166, pp.79-80] where:

the required information is not available;

the behavior is not completely known; and

the existence of measure of variable is noisy.

Soft Computing techniques are widely popular as they are integrated techniques and

highly suitable to find solutions for the problems which are highly complex, ill-

defined and difficult to model.

1.3 Soft Computing for Intelligent System Design

Intelligent System (IS) can be defined as the system that incorporates intelligence into

applications being handled by machines. To incorporate intelligence into machine

applications, characteristics such as reasoning, learning and adaptation are desired.

Apart from these characteristics, search and optimization are the other major abilities

of intelligent systems. In order to deal with complex real world problems and to

impart intelligence; an intelligent system requires combination of knowledge,

techniques and methods from various sources. These systems are supposed to possess

human-like expertise within a specific domain along with the ability to adapt and

learn in dynamic environments. To achieve such a complex goal, a single computing

paradigm is not efficient. Rather, it is observed that the traditional computing

techniques are time consuming and require high efforts in development and

maintenance. The machine applications which deal with imprecision, uncertainty,


5


low-cost solution, partial truth and robustness can be developed by implementing soft

computing techniques. The salient features of soft computing are narrated as follows

[45, p.3]:

An extensive mathematical formulation of the problem is not always required;

Various types of tasks can be performed with variety of methods; and

Provides an adaptive algorithm, to accommodate changes in dynamic

environment.

1.3.1 Major Consortium of Soft Computing

Soft computing is a consortium of computing methodologies that provides a

foundation for the conception, design, and deployment of intelligent systems to

provide economical and feasible solutions with reduced complexity. Figure 1.2

presents major consortium of soft computing.

Figure 1.2: Principal Constituents of Soft Computing Family

Different combinations of techniques from such consortium have provided excellent

results for designing intelligent systems; e.g. Fuzzy Logic (FL), Neural Network

(NN), Evolutionary Computations (EC) and Probabilistic Reasoning (PR). Each of

Soft Computing

(SC)

EC-FL

PR-FL PR-NN

EC-NN

Fuzzy Logic

(FL)

Evolutionary

Computation

(EC)

Neural

Network

(NN)

Probabilistic

Reasoning

(PR)


6


these techniques has their own strengths and limitations. Integration of two or more

techniques can provide significant advantages for intelligent system design.

The hybridization of major constituents of SC can be represented as EC-FL, EC-NN,

PR-FL and PR-NN as shown in Figure 1.2. These constituents are briefly explained as

under.

Evolutionary Computation (EC)

The domain of evolutionary computation involves the study of the foundations and

the applications of computational techniques based on the principles of natural

evolution. Generally speaking, evolutionary techniques can be viewed either as search

methods, or as optimization techniques [27, p.18]. An abstract task can be solved in

such an optimized way that the best solution can be found as a result of search

through a space of potential solutions [205].

Fuzzy Logic (FL)

Fuzzy Logic is a multi-valued logic introduced by L. Zadeh in 1965 [121]. Fuzzy

Logic allows intermediate values to be defined between the two aforementioned

conventional evaluations. Fuzzy systems are based on Fuzzy Logic; a generalization

of conventional (Boolean) logic that has been extended to handle the concept of

partial truth - truth values between “completely true” and “completely false”. Fuzzy

Logic provides an inference mechanism that enables approximate human reasoning

capabilities to be applied to knowledge-based systems [174, p.129].

Probabilistic Reasoning (PR)

The aim of a probabilistic logic (also probability logic and probabilistic reasoning) is

to combine the capacity of probability theory to handle uncertainty with the capacity

of deductive logic to exploit structure. Probabilistic reasoning may be viewed as an

analogous manner to fuzzy reasoning, considering uncertainty in place of fuzziness as

the concept of approximation that is applicable. The Bayesian approach is commonly

used [63, p. 44].

Neural Network (NN)

Neural network being a simplified model of biological neuron system is a massively

parallel distributed processing system made up of highly interconnected neural

http://en.wikipedia.org/wiki/Probability_theory

http://en.wikipedia.org/wiki/Deductive_logic


7


computing elements that have an ability to learn and thereby acquire knowledge and

make it available for use [188, p.11].

The principal constituent methodologies in SC are complementary rather than

competitive. This leads to concepts of hybridization of intelligent systems which is a

promising research field of modern computational intelligence concerned with the

development of the next generation of intelligent systems. In recent years, the

integration of different learning and adaptation techniques has made it possible to beat

individual limitations and to achieve synergetic effects. This fusion has contributed

excessive designs for new intelligent systems. Most of these hybridization

approaches, however, follow an adhoc design methodology, justified by success in

certain application domains [1, p.1].

Soft computing introduces intelligent techniques which are also easy to implement

and less time consuming. The major characteristics of soft computing systems are

briefly discussed as follows [174, p. 243]:

Simulation of Human Expertise

Two pioneer constituents of soft computing i.e. NN and FL provide similarity in

processing to human expertise. Fuzzy Logic is used to process human like

classification of things into group with the representation of fuzzy linguistic variable

while neural network offers learning ability.

Innovative Techniques

Innovative techniques for optimization, self evolutionary solutions, machine learning,

reasoning, and searching from various disciplines like Genetic Algorithms, neural

networks, and Fuzzy Logic are the major contributions of soft computing.

Natural Evolution

Hybridization of Genetic Algorithm with other soft computing components, results in

natural evolution of a solution. An artificial neural network provides mechanism for

self learning and training itself, with or without training data.

Model Free Learning

The problems that cannot be solved using any specific model can easily be solved

using hybridizing soft computing techniques.


8


Goal Driven

Neural networks and Genetic Algorithms are goal driven i.e. only the solution is

important not the path; followed by network/algorithm.

Extensive Numerical Computations

Soft computing provides an extensive computational algorithms offered by neural

network, Fuzzy Logic and Genetic Algorithms unlike traditional symbolic Artificial

Intelligence (AI). As a result, controlling signal processing and non-liner regression

becomes possible.

Dealing with Partial and Incomplete Information

Integration of FL and ANN, provides benefits like ability to deal with incomplete,

uncertain and vague information. The nature of real world problems is always

imprecise, uncertain and randomly changing. Hence, it is obvious to model real life

problems using such techniques.

Soft computing approach is used to support automatic and intelligent decision

making. EC approach is hybridized with Fuzzy Logic for the research work discussed

in this thesis. The prime focus of the research is to provide generic evolutionary

framework that implements evolutionary process which incorporates linguistic

knowledge.

1.4 Evolutionary Computing in Search and Optimization

The search techniques are classified among three major types of techniques i.e.

Calculus Based Techniques, Guided Random Search Techniques and Enumerative

Techniques. Calculus based techniques are branched into two major methods: Direct

and Indirect while Enumerative technique supports Dynamic Programming. Guided

Random Search techniques are branched into Evolutionary Computing and Simulated

Annealing (SA). EC provides four main methods namely Genetic Algorithms (GA),

Evolutionary Strategies (ES), Evolutionary Programming (EP) and Genetic

Programming (GP). Figure 1.3 highlights major search techniques and positions

Genetic Algorithms among them.


9


Figure 1.3: Search Techniques (Source: Fakhreddine & Clarence, 2004)

Traditional search and optimization methods demonstrate a number of difficulties

when faced with complex problems. The major among them are narrated as follows

[135]:

Due to rigid specialized methods for given category of problems; one

algorithm used to solve different problems can become problematic;

Varieties of problems cannot be solved;

Do not provide solution using global perspective but may be trapped in local

optimum; and

Cannot deal with parallel computing environment.

Since, every computational process demands optimization critically, it is desired to

design such algorithm that would be able to satisfy this thrust of optimization of

computational process. Traditional computational methods can provide solutions for

real life problems which may be result of an exhaustive search and expensive resource

consumption. The time and space complexity may be very high but obviously it is less

desirable for handling imprecise variables. Soft computing methods are mainly

developed to remove such problems. Soft computing provides set of hybrid methods

Search Techniques

Calculus Based

Direct Indirect

Guided Random Search

Evolutionary

Computing

Genetic

AlgorithmsEvolutionary

Strategies

Evolutionary

Programming

Genetic

Programming

Simulated

Anneling

Enumerative

Dynamic

Programming


10


to employ search and optimization. This leads to the concept of evolutionary system

approaches.

1.4.1 Importance of Evolutionary Computing

Evolutionary Computation (EC) refers to the computer-based problem solving

systems that use computational models of evolutionary process. It has emerged as

new paradigm for computing, and has rapidly demonstrated its ability to solve real-

world problems where traditional techniques have failed. Evolution has optimized

biological processes; therefore adoption of the evolutionary paradigm to computation

and other problems can help us find optimal solutions from wide-ranging possibilities.

The most significant advantage is parallel processing i.e. testing and changing of

numerous species and individuals occur at the same time (or, in parallel).

Evolutionary computing is utilized in Evolutionary Algorithms (EA). EAs were not

typically designed as machine learning techniques but at the same time, it is well-

known that learning task can be modeled as an optimization problem, and hence

solved through evolution. EAs are successfully applied to a huge variety of machine

learning and knowledge discovery tasks due to capabilities to incorporate existing

knowledge in flexible ways [54]. Successful implementation of broad range of

applications (e.g. robotics, control, and natural language processing), simple way of

implementation involves little domain knowledge, as well as development of multiple

solutions that search different parts of the solution space simultaneously are

considerable contributions provided by EAs [140]. The highly demanding

computational problems such as function optimization, classification, machine

learning, simulations of real time complex systems and many more applications have

been significantly developed by utilizing methods of EA.

In recent years, cognitive systems have gained prominence by implementing

evolutionary approach to the computational modeling. The evolutionary computation

is best suited to following types of computational problems that require following

[104]:

Search through many possibilities to find a solution;

Large search space. Parallel approaches are highly suitable for such problems;

An adaptive algorithm.


11


The basic variations of Evolutionary Algorithm include Evolutionary Computing

(EC), Genetic Algorithms (GAs), Evolutionary Strategy (ES), Genetic Programming

(GP), and Learning Classifier Systems (LCSs). The research work is intended to

design evolutionary framework using Genetic Algorithm.

1.4.2 Role of Genetic Algorithm for Evolutionary Computation

Genetic Algorithm is an evolutionary-based search or optimization technique. It is one

of the prime optimization techniques from the tree of evolutionary search and

optimization shown in Figure 1.3 that performs parallel, stochastic, but direct search

method to evolve the best solution. The area of GA has been traversed by three

prominent researchers namely Fraser in 1962, Bremermann in 1962 and Holland in

1975 [21,80,98]. Genetic Algorithms are pioneered by John Holland in 1970‟s [97].

Genetic Algorithms are based on principle of natural evolution which is popularly

known as “Darwinian Evolution”. They simulate the survival of the fittest among

individuals, encoding a possible solution, over consecutive generation for solving a

problem. A fitness score is assigned to each solution representing the abilities of an

individual to „compete‟. The individual with the optimal fitness score is required to be

found. The entire population evolves towards better candidate solutions via the

selection operations and genetic operators such as crossover and mutation. The

selection operator decides which candidate solutions move into the next generation,

which limits the search space. The cross over and mutation operators generate new

candidate solutions from the search space. In this way it is hoped that over successive

generations better solutions will thrive while the least fit solutions die out. Eventually,

once the population has converged and is not producing offspring noticeably different

from those in previous generations, the algorithm itself is said to have converged to a

set of solutions to the problem at hand. Genetic Algorithms are widely used in

engineering, scientific as well as business applications. They are successfully applied

to the problems which are difficult to solve using conventional techniques such as

machine learning and optimization.

It is observed that GA provides following major advantages [202]:

GA can be easily interfaced to obtainable simulations and models;

GA is easy to hybridize and easy to understand;

GA uses little problem specific code;


12


GA is modular, separate from application;

GA is capable to obtain answers always and gets better with time; and

GA is inherently parallel and easily distributed.

1.5 Need of Hybridization of Genetic Algorithm with

Fuzzy Logic

The major benefit of Genetic Algorithm is that it can be used to find optimized values

from large search space as well as makes system able to learn. At the same time, the

major limitation of Genetic Algorithm is that it is not capable to store domain

knowledge but GA can be used to find optimized values for the membership function

parameters, particularly when manual selection of their values becomes difficult or

takes too much time to attain [63, p. 390].

In order to solve problems of real life applications, it is required to deal with

imprecise or inexact knowledge. Prof L. Zadeh proposed the theory of Fuzzy Logic

which is designed to handle uncertainty and imprecision. According to Zadeh, degree

of knowledge representation can be enhanced with the use of linguistic variables

[122]. Fuzzy systems are based on Fuzzy Logic and fuzzy set theory which provide a

rich and meaningful addition to standard logic. O. Cordo´n et al. [166, p.2] state that

“Conventional approach of knowledge representation uses bivalent logic which has

major shortcomings like handling imprecision and uncertainty”. One of the most

popular types of fuzzy systems is Rule Based (RB) system; this has been successfully

used to model human problem solving activity and adaptive behavior by using the

simplest form of knowledge representation with if-then-else rules. The values of the

linguistic variables are defined by context dependant fuzzy sets whose meanings are

specified by gradual membership functions [53]. Another type of fuzzy system is

fuzzy classifier system. It is a machine learning system which employs linguistic rules

and fuzzy sets in its representation. The major reasons behind fuzzy systems

development are enlisted as follows [89]:

Mimic human reasoning;

Fulfill a need for a mathematical model;

Relatively simple, fast, and adaptive; and


13


Mathematically difficult design objectives can be implemented easily using

linguistic or descriptive rules.

Fuzzy Logic based system finds a wide range of applications in various industrial and

commercial products and systems. These areas include most of the control

engineering systems, machine learning systems as well as hybrid systems for

medicines, production, economics, human resources, etc. with integrated fuzzy

components. One of the most important tasks in the development of fuzzy systems is

the design of its knowledgebase. But the major limitations of fuzzy systems are

enlisted as follows:

Inability of self learning, adaption or parallel computation;

Cannot support optimization; and

Answer obtained once cannot get better with time.

In order to solve the stated problems, particularly in the framework of soft computing,

significant methodologies have been proposed with the objective of building fuzzy

systems by means of Genetic Algorithm.

The research work implements the theory of FL for classifying the range of crisp

values from an application domain. Here, linguistic rule representation is produced in

order to design fuzzy inference mechanism. It therefore combines an easily

understood representation for a general purpose search method. Here, decision

includes variety of classes based on multiple values within a range. This type of

multi-valued classification cannot be possible through bivalent logic.

The major benefit of GA is that it can be used to find optimized values from large

search space as well as makes system able to learn. In order to handle imprecisely

stated information, fuzzy decision making approach has been utilized. As a part of

machine learning approach, the system has been designed in such a way that it can

integrate linguistic knowledge with GA and make system self lean. The evolutionary

fuzzy system is designed such a way that it becomes less computationally expensive

for achieving optimum solution.


14


1.6 Educational Perspective of Theory of Multiple

Intelligence

The major goal of education is to increase the level of intelligence in every individual

for progressing in respective professions. Education field has introduced numerous

trends in different areas of human life. This resulted in generation of more

employment and increased demand for multiple skills. Information and

Communication Technology (ICT) plays an important role in educating and

improving skills of individuals and helps them in improving their problem solving

capabilities.

Genetically every individual is blessed with multiple types of intelligence. These

intelligences are found available in each individual in different capacities. However,

results of many researchers have shown that appropriate training and development

methods can increase the level of intelligence by utilizing instructional technologies

[115]. There are various theories invented by many researchers to identify the types of

intelligence in human beings. The modern theory says that human intelligence is not

limited to one or two directions but there are several other equally important and

valuable aspects of intelligences which are required to be recognized and developed.

The fact is that no one is talented in every domain and no one is completely

incompetent in every domain. So, level of different types of intelligence is different in

every different human being.

Out of the different theories available to identify and enhance human intelligence,

Theory of MI is pioneer among researchers and educationalists. Dr. Howard Gardner

has developed Theory of Multiple Intelligence (MI), which defines intelligence as

potential ability to process a certain sort of information [170, p.5]. In 1993, Gardner

has identified nine intelligences but there is also a possibility of many other types of

intelligence in individuals [72]. Figure 1.4 shows set of intelligence of Theory of MI.


15


Figure 1.4: Multiple Intelligence Model

1.7 Problem Context

Modeling decision making processes on extensive data which concern human

decision have become possible through modern data base management systems.

However, these data are, just a collection of recorded facts that do not contain by

themselves any information or knowledge useful to explain or to predict the decisions.

There exist several methods that can design the system to predict the outcome based

on such extensive data. Even though useful and widely used, these methods and

systems are deficient in the explanatory power and providing automatic decision.

A major class of problems in education domain involves the classification of skills

based upon various criterion performed upon the students. Traditionally, data analysis

to retrieve the knowledge by analyst(s) was a manual process while the computerized

information systems with automatic decision support is today‟s requirement. The

hybridization between Fuzzy Logic and GAs, called Genetic-Fuzzy Systems (GFSs),

has attracted considerable attention in the computational intelligence community.

Their flexibility and capability to incorporate existing knowledge are also very

interesting characteristics for the problem solving. As a literature review of existing

Theory of Multiple

Intelligence

Logical

Verbal

Inter

Personal

Intra

Personal

MusicalVisual

Kinesthetic

Moral

&

Existential

Naturalist


16


intelligent decision support systems; it has been pragmatic that there is always a need

of generalized as well as evolutionary framework which also supports multi-valued

classification in easy and understandable manner. It has been always the need from

the educational perspective to measure different capabilities of human beings. Theory

of MI has been a motivation to design the research application. It is always desirable

to identify different abilities of human being and then utilized in the appropriate field.

The research work focuses on measurement of logical, verbal, interpersonal,

intrapersonal and musical intelligence of various students based on Theory of

Multiple Intelligence model. The problem is to suggest the suitable career field of the

students according to level of different types of intelligence he/she possess.

1.7.1 Justification of Research Problem

Major goal of education is to increase level of intelligence in every individual to

progress in all areas. Technological advancements increase the efficiency of decision

making and problem solving of human being. To deal with real life problems, certain

level of intelligence is essential for every individual. This has been made possible by

technological advancements. It has been observed that many times an individual

himself cannot identify his own interest and capabilities in specific areas. It has been

observed that many times students cannot really identify their capabilities and due to

lack of such knowledge; they might select wrong professional fields. In such cases,

the users may not get complete advantage of their capabilities. The solution of this

problem is to identify the exact capabilities of an individual since childhood. For

optimum utilization of opportunities and to get strategic advantages; it is important to

analyze and enhance level of intelligence.

The literature review of the research work brings following points into observation;

which are as under.

The major professional domains such as education and technology, human

resources, psychology, etc, still lack practical and intelligent decision support

system to achieve efficient and powerful classification of human capabilities.

There is a lack of generalized framework implementing Theory of MI in order

to analyze students‟ (i.e. user‟s in a generalized manner) different skills using

evolutionary fuzzy approach.


17


There are many computer based applications developed so far to identify and

then enhance different types of intelligence using the Theory of MI. However,

the system that can automatically identify specific level of specific type of

intelligence using evolving knowledge-base approach through Genetic- Fuzzy

system is yet to be developed.

In order to achieve machine learning and automatic decision making; evolutionary-

fuzzy approach is utilized. The research work is based on supervised machine

learning approach in which machine is trained through expert‟s knowledge once, and

later on takes decision automatically every time. One of the member of syndicate of

SC; Fuzzy Logic is selected to deal with multi-valued classification. A novel genetic

system has been designed which integrates linguistic knowledge representation and

finally provide the best possible solution automatically. A prime target for such

intelligent system is the implementation in the domain of Theory of Multiple

Intelligence which analyzes and enhances multiple capabilities in human beings.

1.8 Objectives and Contribution of Research Work

It has been observed that there are several decision support systems available to

identify the skills of users/students using different criteria. However, the existing

solutions and systems lack human like decision support and other characteristics that

exhibit form of intelligence. To meet the stated objectives, a generic framework is

decided to be developed that identifies skills of students and provide decision support

accordingly. Two major outcomes are contributed by the dissertation are enlisted as

follows:

Generic framework that hybridizes Genetic Algorithm and Fuzzy Logic; and

Classification of different types of skills based on Theory of Multiple

Intelligence.

One of the major tasks of analytical educational system is classification of skills of

students. It has been observed that bivalent logic provides only binary classification

i.e. students are either dull or clever. However, the level of such dullness or cleverness

is not possible to measure using bivalent logic. Fuzzy Logic satisfies the need of exact

decision support by providing multi-valued logic. Fuzzy classification has not often


18


been applied to Theory of Multiple Intelligence using evolutionary approach,

although it seems to be suited to improve user‟s intelligence.

This forms the first research question: „what‟ is the level of intelligence? and „how‟ it

is to be analyzed? Until now, no generalized approach using Evolutionary Algorithm

has been developed in a problem domain of Multiple Intelligence for student

segmentation using several criteria. Hence, using the aforementioned framework,

classification of users according to their skills based on the Theory of Multiple

Intelligence is required; which is the second research problem.

To solve the above stated research problem, the following objectives are set:

To document and use knowledge of human expert in the form of rules with the

help of the Theory of Multiple Intelligence.

To develop a generic framework that provides advantages of Genetic Algorithm

as well as Fuzzy Logic. The framework incorporates evolutionary process

responsible to evolve fuzzy rules.

This phase has following sub-objectives:

To develop encoding scheme for knowledge representation;

To develop a selection process and fitness functions to obtain the convergence

criteria; and

To design genetic operators which further optimize the system and evolve off

springs till it reaches convergence.

To further enrich the system, the aforementioned framework is to be implemented

with more facilitating interface. Finally, evolutionary framework is integrated

with whole decision making process.

To be agile, accommodate new inventions of Theory of Multiple Intelligence in

the form of fuzzy representation.

To document the findings in the form of publications such as research papers,

popular articles and books for future use and training.

To achieve the aforementioned objectives; a genetic-fuzzy hybrid model has been

developed, which forms a base of an intelligent system design using evolutionary

approach. As stated, the main objective of this dissertation is to develop, implement,

and validate a framework utilizing a hybrid genetic-fuzzy approach and a research


19


prototype of an intelligent system for decision support in identifying degree of

different types of intelligence in users. Particularly, the emphasis of this research

framework is on the development of the generic research paradigm that could be used

as an instrument for development of various intelligent systems in different domains

by depositing the knowledge, experience, and decision of human expert.

In order to fulfill the need of automatic decision support, the approach involves

dealing with various important aspects of the problem through intensive utilization of

prominent soft computing techniques such as Genetic Algorithm and Fuzzy Logic. As

a result, the system will be self learning and capable to provide the decision

automatically based on supervised learning approach with the help of genetic-fuzzy

hybridization. The designed Genetic-Fuzzy system possesses following

characteristics:

The crucial factor of a hybrid system framework is the integration of

knowledge representation in an evolutionary manner. Fuzzy representation

and genetic components like, encoding schemes, fitness function and

specialized operators are designed for the application domain of Theory of

Multiple Intelligence.

The designed intelligence system is human-friendly, exhibiting good

interpretability.

The system is capable to identify and classify the presented cases correctly.

Users from different application areas such as Medical Diagnostics,

Engineering, Education, Sociology, Human Resources, Psychology, Network

Monitoring, etc. will be benefited by the designed generic framework of the

system. Eg. in educational domain, this research prototype can be useful for

training of teachers and students, different types of resource planning, etc.

1.9 Challenges Involved

The designed intelligent system focuses on novel Genetic-Fuzzy generic framework

deals with following challenges:

1. Design and implementation of genetic representation

In GA, the genes and alleles must be designed for the specific problem and must

express the various components of a potential solution. Unfortunately, there are no


20


straightforward, general purpose approaches for the same. The task is highly

dependent on the problem itself. Hence, the design of generalized genetic

representation scheme is crucial to design intelligent system problem context of

Theory of MI.

2. Design of fitness function

A major challenge is to define appropriate fitness function that serves as an adequate

representative of the optimization process. The objective function is not only problem

specific but it is also inherently specific to the genotype used to represent the

solutions. The problem deals with identification of human intelligence through GA.

For such kind of domain, where application itself is not dependent on any

mathematical formula, it is a challenge to define objective function. Hence, designing

the objective function is one of the most challenging and also one of the most

important steps in working with GA.

3. Design and implementation of genetic operators

There are three basic operators responsible for evolving nature of GA i.e. Selection,

Crossover and Mutation. Each of them has several subtypes available. The genetic

operators are generalized but they can be designed as per the requirements of

optimizing the outcome. Here, one of the primary challenges is to design specialized

genetic operators for specific application which lacks mathematical formulation but

require optimal outcome and fast convergence rate.

1.10 Organization of Thesis

The research work has been organized into seven chapters which are briefly discussed

as follows:

Chapter 1 introduces hard computing techniques along with its inappropriateness for

real life applications. It also compares soft computing techniques with hard computing

techniques. The classification taxonomy of computational approaches is also

highlighted. The chapter justifies various needs of soft computing methods for

designing intelligent systems. Chapter 1 has identified the problem and the objectives

of the study. It discusses several possible contributions made by the research work.

Some features of Fuzzy Logic and Genetic Algorithm are also presented. It justifies

the need of research problem and also proposes the approaches undertaken to solve


21


the same. In relation to the work performed in this study, the remainder of the report

consists of the following chapters;

Chapter 2 provides detailed description of the computing methods used for research

work. The first section of the chapter broadly outlines Evolutionary Algorithms for

machine learning. It presents importance of EA including justification of GA as one

of the prime components of Evolutionary Computing. The general structure of

classical Genetic Algorithm is presented along with its structural components. The

second section presents difference between traditional logic and Fuzzy Logic. It

presents significance of linguistic knowledge representation using Fuzzy Logic. It

also discusses the need of hybridization of Fuzzy Logic for evolutionary computation

(EC). It presents literature review of Genetic Algorithms and Fuzzy Logic. The third

section deals with the domain of the research work. Here, the significance of

education is discussed as one of the prime factors among the several factors affecting

human life. It narrates how intelligence can be utilized in achieving professional

success in human life. Extensive literature review on the theory of Multiple

Intelligence has been discussed. It also justifies the need of development of research

prototype application.

Chapter 3 presents the significant design views of genetic learning. In order to

achieve hybridization of Fuzzy Logic with Genetic Algorithms, several approaches

have been considered. Here, the applications developed based on existing approaches

are enlisted. The extensive review on genetic rule learning is presented as a part of

evolutionary fuzzy modeling. The major Genetic-Fuzzy modeling approaches i.e. The

Michigan, The Pittsburg, The Iterative Rule Learning (IRL) and Genetic Cooperative-

Competitive Learning (GCCL) are discussed with their characteristics. Here, issues

related to shortcomings of the existing research are presented. Finally, the chapter

justifies the need of research work to be carried out.

Chapter 4 presents generic framework for measuring the multiple intelligence of

students using an evolutionary fuzzy approach. The chapter is broadly classified into

major four sections. The chapter discusses the need to design generic framework, as

well as features provided by intelligent systems compared to traditional systems.

Here, the significant characteristics of an intelligent system are presented. It

represents generic framework of evolutionary-fuzzy system which is designed as a


22


part of research work. The chapter elaborates working characteristics of every

component of designed evolutionary fuzzy framework.

Chapter 5 focuses on detailed design methodology of research work for generic

evolutionary framework in order to design intelligent system. This framework is

based on novel design of hybridization of Genetic Algorithm with linguistic

knowledge. The chapter presents algorithm of implementing generic framework along

with discussion of every steps. The chapter further elaborates the need of genetic

operators such as crossover, mutation and algebraic operator in order to get the

optimized results with minimum computational criteria.

Chapter 6 deals with the crux of the dissertation. It presents discussion on the results

achieved as prototypical implementation of evolutionary fuzzy hybrid model. To

enrich the system and for ease of use by academic staff, a demo version of MATLAB

7.0 based GUI tool is also developed. This GUI version incorporates the complete

architecture of the intelligent system as discussed in Chapter 5 and is quite intuitive to

use by users considering possibility of their limited exposure to GA Fuzzy hybrid

system. The chapter presents different results using different types of charts for

achieving intermediate outcomes as well as the final outcome.

Chapter 7 represents the concluding part of the research work. The chapter focuses

on the contributions made by the work presented in this thesis in the fields of

computer science and education. The work incorporates two major distinctive

constituents of soft computing: Genetic Algorithm and Fuzzy Logic. This integration

provides strong foundation to design of an intelligent system, which can

independently propose the future career fields of a student after analyzing level of MI

in a particular student. The benefits of designed intelligent system are presented

which include knowledge documentation, automatic decision making, flexibility and

ease of use, machine learning and automatic evolution of rules, cost effectiveness and

contribution to the domain of Multiple Intelligence as well as other application areas.

The chapter also discusses future scope along with suggestions for further

improvements possible in this current research work. The concluding part of the

chapter justifies significant role of the designed framework and how challenges have

been fulfilled by the research work. Finally, the chapter concludes with the aims and

objectives achieved by the research. Figure 1.5 shows the structure of the thesis which

outlines flow among the chapters.


23


Figure 1.5: Flow of Chapters of Thesis

5. Detailed Design Methodology

(Steps of Designed Algorithm, GA System Architecture, and Detailed

Discussion on Components of GA–Fuzzy Hybrid System, Sequence of

Application of Operators, Convergence Criteria for Designed GA System)

1. Introduction

(Soft computing for intelligent System design, SC constituents, Need of

GA fuzzy Hybridization, Education Perspective of Theory of MI, Problem

Context, Objectives and Contribution of Research, Challenges Involved,

and Organization of thesis)

2. Literature Review

(Concepts of EA, GA,

FL and Applications

Developed in Area of

GA and FL, Role of

Education, MI and

Work done in MI)

3. Evolutionary

Fuzzy Modeling

(Genetic-Fuzzy

Hybrid Models and their

Roles in Real Life

Applications)

4. Measuring MI using Evolutionary Fuzzy Model: A Generic

Framework

(Importance of Intelligent System, Need to Design Generalized

Framework, Generic Framework using Evolutionary Process for

Measuring MI, Detailed Discussion on Components of Generalized

Framework)

6. Discussion on Results

( A Brief Discussion on Designed User Interface for Prototypical

System, Training Data Set, Test Data Set, Implementation of GA using

Various Values)

7. Conclusion

(Overall Contribution of Research, Contribution to Evolutionary

Computation, Advantages of Research Work, Applications of Generic

Framework in Other Areas, Future Scope of Research Work)

Date post:	19-Apr-2018
Category:	Documents
Upload:	vanduong
View:	237 times
Download:	4 times

Evolutionary Fuzzy Logic Computation -...

Documents