GA FL Theory of MIshodhganga.inflibnet.ac.in/bitstream/10603/34784/11/11_chapter2.pdfcorresponds to...

Chapter 2: Literature Review

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A Genetic-Fuzzy Approach to Measure Multiple Intelligence


Theory of MI

FL

GA


25


2.1 Chapter Overview

A review of the literature was undertaken for the purpose of determining what

information had been previously documented about the broad topics of evolutionary

computation, Fuzzy Logic and the domain of Theory of Multiple Intelligence (MI).

The literature review has been organized around three main themes. In the first

section, the chapter presents role of evolutionary algorithm along with characteristics.

This section discusses Genetic Algorithm as the prime evolutionary methods for

machine learning, search and optimization. Here, life cycle of Genetic Algorithm is

explained along with discussion of its components. The chapter discusses the prime

constituents of Genetic Algorithm i.e. encoding schemes, fitness functions and genetic

operators in detail. The last part of the first section narrates work done so far in

different application areas such as pattern recognition, traveling sales person problem,

optimization in different types of scheduling, machine learning, hybridization with

other soft computing constituents, etc. The second section of the chapter introduces

the basic concepts of Fuzzy Logic. It provides motivation for utilizing the theory of

Fuzzy Logic to provide multi-valued solutions. Comparison of traditional bivalent

logic with Fuzzy Logic as well as working characteristics of fuzzy rule based system

is presented. Fuzzy modeling in various domains has been extensively used in order to

achieve rule learning, classification, industrial automation and consumer products,

rice taste eveluation,etc. The third section focuses on application domain of the

research work. Here, the Theory of Multiple Intelligence is chosen as the main

application domain and its significance in the field of education is presented. The

chapter justifies the role of education for every human being in achieving professional

success. The chapter further elaborates modern research on human intelligence and

processes of intelligent thinking. Comparison between traditional view of human

intelligence and modern view is also presented. The chapter discusses different

constituents of Theory of Multiple Intelligence (MI). Finally, this section of the

chapter concludes with narrating significant applications which have been developed

based on Theory of MI and also identifies the need of current research work.

2.2 Evolutionary Algorithms

Evolutionary algorithms (EA) are popular search techniques that can search for an

enormous problem spaces. Evolutionary computation (EC) offers mechanisms that


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gave rise to adaptations present in existing living systems to the biologist, ethnologists

or psychologist. The genetic search can be resultant in a powerful method of problem

solving. If we accept the often-repeated slogan, “Nature is smarter than we are”, then

harnessing the problem-solving methods that nature uses makes sound practical sense

[43]. The driving force behind EA is the selection of individuals based on their

fitness. The fitness function evaluates the solution and returns a numerical answer.

Analysis of fitness function decides the quality of solution. The individuals with a

higher fitness have a higher probability to be chosen as the members of the population

of the next iteration (or as parents for the generation of new individuals). This

corresponds to the principle of survival of the fittest in natural evolution. It is the

capability of nature to adapt itself to a changing environment, which gave an

inspiration for EA. EA methods are designed for highly demanding computational

problems such as function optimization, classification, machine learning, simulations

of real time complex systems and many more applications. With the explosive growth

of the EA field, there has also been an expansion in the variety of EA types.

There are a number of evolutionary techniques whose main similarity is the use of a

population of random or pseudo-randomly generated solutions to a problem. At each

of the iteration, a number of operators are applied to the individuals of the current

population to generate the individuals for the next generation population. Usually, EC

algorithms use an operator called recombination or crossover to recombine two or

more individuals to produce new individuals. They also use mutation or modification

operators which cause a self-adaptation of individuals. The main categories of

selection methods are as follows:

Artificial selection: A selection process is designed such a way that it can

retain or eliminate specific features according to a goal.

Natural selection: A selection process is similar to the Darwinian Theory of

biological evolution. In natural selection process, there is no actor who does

the selection. The selection is purely automatic or spontaneous without any

predefined logic.

As a result, evolution generates greater complexity. Different evolutionary techniques

are available in order to utilize generated solutions of a problem [116].


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2.2.1 Basic Evolution Cycle

Evolutionary computing provides several procedures which are capable of reducing

almost all the kinds of the limitations; which could not be properly handled by

conventional algorithms. Figure 2.1 displays basic evolution cycle.

Evolution cycle consists of several interactions in order to generate EA as shown in

Figure 2.1. The process starts with initialization/selection of current population, which

works as parent chromosomes for Genetic Algorithm. Selection criteria are applied on

this entire population to select the best individuals which acts as parents for next

generation. Genetic operators like crossover / mutation are applied to generate off-

springs to act as a new generation of the population. Fitness mechanism is applied on

the entire population. Based upon the fitness, the lesser fit population is removed and

the process is repeated until desired fitness is achieved.

The quality of solutions can be improved with the help of appropriate choices of a set

of algorithmic, strategic and related parameters. In order to achieve such qualitative

solutions, system should be passed through a reasonably small number of iterations

and samples that have been chosen from a large search space.

The characteristics of EA are enlisted as follows [63, p.368)]:

Provides high rate of convergence;

Provides good quality of evolved solution; and

Provides reasonable computational requirements.

Figure 1.1: Basic Evolution Cycle

Start

Current

Population

Modified

Population

Fit

nes

s Selected

Individual

s Operations

like

Crossover

and

Mutation

New Generation

Selection


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So far, no general analysis framework has been proposed to analyze the general form

of evolutionary algorithm [63, pp.368].

2.3 Genetic Algorithm- An Important Variant of EA

As a result of the extensive research of Holland and his colleagues; a class of methods

of search directly inspired by biological systems has been invented. This form of

algorithm is popularly known as Genetic Algorithms [40, 98]. The basic techniques of

the GAs are designed to simulate processes in natural systems necessary for

evolution; especially those follow the Darwinian principle of „survival of the fittest‟.

GA represents a new programming paradigm that tries to mimic the process of natural

evolution to solve computing and optimization problems. Initially, research in GAs

remained largely theoretical until the mid-1980s. Later, Kenneth De Jong's

foundational work established more widespread interest in the evolutionary

computation [110]. By the early tomid-1980s, Genetic Algorithms were being applied

to engineering issues such as pipeline flow control, pattern recognition and

classification, and structural optimization. Today, evolutionary computation is a

thriving field, and Genetic Algorithms are „solving problems of everyday interest‟

such as stock market prediction and portfolio planning, aerospace engineering,

microchip design, biochemistry and molecular biology, and scheduling at airports and

assembly line [22].

Genetic Algorithms are computerized search and optimization algorithms based on

the mechanisms of natural genetics and natural selection. Essentially, Genetic

Algorithm is an optimization technique that performs parallel, stochastic, but direct

search method to evolve the fittest population. It is a method for solving both

constrained and unconstrained optimization problems. In some cases, the simulated

evolution of a solution through Genetic Algorithms is proven more efficient and

robust than the random search, enumerative or calculus based techniques [40].

There are significant differences observed between GA and most of the traditional

optimization algorithms as summarized by [63, p.379;154, p.463; 188, p.228]:

GA uses a population of multiple points at single run while traditional

optimization method uses single point approach;

GA converts design space into genetic space;


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GA works with coding of parameter set rather than actual value of parameters.

This discretizes the search space even though the function may be continuous;

GA can be robustly applied to problems with any kinds of objective functions,

such as nonlinear or step functions; because only values of the objective

function for optimization are used to select genes;

GA can have less chance to be trapped by local optima due to characteristics

of crossover and mutation operators; and

GA uses stochastic reproduction schemes rather that deterministic ones.

According to Maxim, several weaknesses of Genetic Algorithms have been noticed

such as [25]:

Domain knowledge cannot be directly documented using GA structure.

GA requires entire population to work (takes lots of time and memory) and

may not work alone well for real-time applications.

GA cannot deal with imprecision and uncertainty which are considered the

basic requirement of real life applications.

2.3.1 Basic Structure of Genetic Algorithm

The life cycle of GA is presented as shown in Figure 2.2. The life cycle is discussed

as under.

GA is an iterative procedure that involves a population of individuals, each one

represented by a finite string of symbols, known as the genome, encoding a possible

solution in a given problem space. The classic Genetic Algorithm starts with a

completely random population of solutions. Initially, these populations are evaluated

and among these populations, high fitness individuals are preserved through selection.

In such cases, if desired solution is achieved from expected solution space, algorithm

can be terminated and in other cases which do not provide optimized solution, genetic

operators are applied.


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There are several genetic operators such as crossover, mutation and selection

responsible to combine partial solutions called building blocks from different strings

onto the same string [174, p.218]. This process is repeated until stronger solutions are

generated. As the algorithm moves further, the weaker solutions are incapable to pass

on their genes and not selected for breeding and finally discarded. Stronger solutions

may breed many times, and produce many children that share their qualities [63,

p.382]. The general lifecycle of GA is can lead to solution of the problem, irrespective

of the form of objective function.

1.Reproduction

2.Crossover

3.Mutation

No

Yes No

Yes

New

Cycl

e

Terminate Algorithm

Generate a new population with

significantly fit and strong

elements

Initial Population of

Chromosomes

Initial Fitness Evaluation

Objectives

Achieved?

Apply Genetic Operators

Insert children into the population

and evaluate the new fitness

Objectives

achieved?

Figure 2.2: Life Cycle of Genetic Algorithm


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2.3.2 Terminology Used in GA

The following section briefly explain various terminologies used in the working of

Genetic Algorithm.

Search Space

Basically, the search space is the space of all feasible solutions (the set of solutions

among which the preferred solution resides). It is always desired to look for some

solution which will be the best among others. Each point in the search space

represents one possible solution. Each possible solution can be identified by its fitness

value for the problem. With GA, the best solution can be found among a number of

possible solutions represented by one point in the search space.

The Figure 2.3 represents the example of search space where maximum and minimum

both values of objective function are shown from total search space.

The process of finding such solution is similar to find some extreme value (minimum

or maximum) in the search space. The search space may be well defined, but usually

few points are known in the search space. As evolution proceeds, GA uses the process

of finding solutions that generates other points or other possible solutions. It contains

population of individuals [144].

Population

In order to map natural evolution into the framework of artificial evolution, it is

required to encode concern individuals in form of data. In nature, these data consists

of living creatures. Each individual represents a potential solution to the problem of

.

.

Variable Set

Min Value

Max Value

Obje

ctiv

e F

unct

ion

Val

ue

Figure 2.3: Example of Search Space


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survival. Similarly, in GA, a set of potential solutions, are collectively known as

population. Each single solution is called an individual. Each individual is coded as a

finite length vector of components or variables.

Genotype

It is a collection of genetic characteristics, commonly known as genotype. In GA, the

term genotype is used to describe the encoding of a solution of a problem represented

by an individual. Thus, each individual has genotype which encodes a solution. It is

represented by strings which are called bits or characters.

Chromosome

All living organisms consist of cells. In each cell, there is a set of chromosomes which

are the strings of DNA. In GA literature, an individual genotype is referred to as its

chromosome.

Gene and alleles

It is the fundamental building block of the chromosome, each gene in a chromosome

represents each variable to be optimized. It is the smallest unit of information. Each

gene encodes a particular pattern. Each gene has one or more possible values known

as alleles, e.g. color of human eyes is considered as gene and possible values like

bluish, brown, etc. are alleles.

Locus

Each gene has its own position in chromosome search space. This position is called

locus.

Convergence

The process of breeding a population of Genetic Algorithm over a series of

generations to arrive at the chromosome with the best fitness value is known as

convergence. Convergence is controlled by the fitness function.

Generation

A Genetic Algorithm creates a new population of candidate solutions until a

termination condition is reached. Each new population is known as a generation.


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Termination Conditions

A Genetic Algorithm must be told when to stop searching for a solution. The criteria

for stopping are called termination conditions.

2.3.3 Encoding Schemes

There are a few encoding schemes available for Genetic Algorithm solution

representation. A brief discussion on basic encoding schemes is presented as under.

Binary Encoding

Binary encoding is the most common and simplest encoding scheme. Every

chromosome is represented as strings of bits, 0 and 1. For example, Chromosome “A”

and Chromosome “B” can be represented using binary encoding as shown in the

Figure 2.4(a) and the Figure 2.4 (b).

Chromosome “A”:

0 1 1 1 1 0 1 1 0 0 0 1 0 0 1 1

Figure 2.4(a): Parent 1 using Binary Encoding

Chromosome “B”:

1 0 1 1 0 1 0 1 1 0 1 1 0 1 0 1

Figure 2.4(b): Parent 2 using Binary Encoding

Permutation Encoding

In permutation encoding, every chromosome is a string of numbers, which represents

number in a sequence. Permutation encoding can be used in “ordering problems”,

such as traveling salesman problem or task ordering problem.

Chromosome “A”:

1 5 3 2 6 4 7 9 8

Figure 2.5(a): Parent 1 using Permutation Encoding

Chromosome “B”:

8 5 6 7 2 3 1 4 9

Figure 2.5(b): Parent 2 using Permutation Encoding


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Direct Value Encoding

Direct value encoding can be used in problems where some complicated values such

as real numbers are used. In value encoding, every chromosome is a string of some

values. Values can be anything connected to problem, form numbers, real numbers or

charts to some complicated objects. Eg. the Figure 2.6 (a),the Figure 2.6 (b) and the

Figure 2.6(c) presents different styles of chromosomes using directly value encoding.

Chromosome “A”:

Red Black Blue White Yellow Green

Figure 2.6(a): Chromosome “A” using Direct Value Encoding

Chromosome “B”:

1.8765 3.9821 9.1282 6.8344 4.116 2.192

Figure 2.6(b): Chromosome “B” using Direct Value Encoding

Chromosome “C”:

A B C K D E I G H W

Figure 2.6(c): Chromosome “C” using Direct Value Encoding

Tree Encoding

In order to create evolving programs or expressions for genetic programming, tree

encoding is used. In tree encoding, every chromosome is a tree of some objects, such

as functions or commands in programming language e.g. The Figure 2.7 shows the

tree encoding with two different operational structures.


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2.3.4 Fitness Function

In nature, relative ability of the creatures to survive in the environment is popularly

known as survival of the fittest. A fit creature must be able to find food and protection

along with adjustment of itself in the overall environment. Another creature if capable

to do this better than previous one, it can be called “fitter”. This principle of nature

has been mapped into Genetic Algorithm. To use GA, it is necessary to provide

mechanism for evaluating the value or goodness of a particular solution. This leads to

the concept of fitness function which measures fitness of a particular solution. In GA,

fitness functions are also known as objective functions. Fitness is relative measure

rather than absolute and used to discriminate instead of discovering the ideal solution.

An objective function takes genotype as its parameter and gives real valued result that

represents fitness or designed quality of solution. The characteristics of objective

function can be described as follows [63, pp.374-375]:

The objective function is not only problem specific but also essentially

specific to the genotype used to represent the solutions.

The objective function may be expensive to calculate or may simply suggest

an untrustworthy comparison of two solutions for many problems.

The values of an optimal solution may not be known, even though the

objective function itself is known in some cases.

The objective function may not be mathematical at all, but a measurement

taken for the solution‟s performance in a simulation or real world experiments.

Do Until

Step Wall

Figure 2.7: Operational Structures of Tree Encoding


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2.3.5 Genetic Operators

There are many genetic operators which are responsible to generate process of

evolution using GA. Examples of such operators are selection, crossover and mutation

[186, p.478; 188, p.253]:

2.3.5.1 Selection

This procedure is applied to select the individuals that participate in the reproduction

process to produce new generations. Here, good chromosomes on the basis of their

fitness values can be selected and produce a temporary population, namely, the

mating pool. The selection operator is responsible for the convergence of the

algorithm. This is achieved by the different schemes discussed as under:

Proportionate /Roulette-wheel Selection

Here, the probability to select a string in the mating pool is proportional to its fitness.

It is implemented with the help of roulette wheel. The surface area of the wheel has N

parts where N is population size in proportion to the functional values (f1,

f2…fn).The wheel is rotated in a particular direction either clock wise or anti clock

wise. And a fixed pointer is used to indicate the winning area, after it stops [45, pp.32-

33].

As a result of selection process mentioned above, it may happen that a good string

may be selected many times.

𝑝 =fi

fi𝑛𝑘=1

Scaling Selection

As the average fitness of the population increases, the strength of the selective

pressure also increases and the fitness function becomes more discriminating. This

method can be helpful in making the best selection later on when all individuals have

relatively high fitness and only small differences in fitness distinguish one from

another [16].

Tournament Selection

The tournament size n (eg. 2 or 3) is selected at random which is a smaller size than

population size N. From n random strings from population, the best one in terms of


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fitness value is determined. The best string is copied into mating pool and then all n

strings are returned to the population. Thus, only one string is selected for tournament

and N tournaments are to be played to make size of mating pool equals to N [45,

p.35].

Rank Selection

Each individual in the population is assigned a numerical rank based on fitness and

selection is based on this ranking rather than absolute difference in fitness. The

advantage of this method is that it can prevent strong individuals from gaining

dominance early at the cost of less fit ones, which would reduce the population's

genetic diversity [16].

Elitist Selection

The elitist selection method is proposed by Kenneth De Jong [109]. Here, an elite

string is recognized first in a population of strings. It is than directly copied into next

generation.This method selects fit members from each generation. Usually, pure

elitism is not used by elitist selection but a slightly modified form can be used which

selects the single best individual from a few individuals of each generation.

Generation Gap and Steady-State Selection

Basically, the generation gap is defined as the proportion of individuals in the

population, which are replaced in each generation. It is a regular approach of

replacing individual in each generation [188, p.249]. Steady state selection was

introduced by Whitley [50, 51]. It is a modified version of such method. In this

method, the offspring of the individuals selected from each generation go back into

the pre-existing gene pool, replacing some of the less fit members of the previous

generation. Some individuals are retained between generations.

Hierarchical Selection

Individuals go through multiple rounds of selection in each generation. Lower-level

evaluations are faster and less discriminating, while higher levels are evaluated more

rigorously. The advantage of this method is that it reduces overall computation time.

2.3.5.2 Crossover

The crossover operator is the core genetic operator. It mates chromosomes in the

mating pool by pairs and generates candidates of offspring by crossing over the mated


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pairs with probability. It can generally be classified into three major categories [40,

66, 188, 205]:

1. Conventional Operators

Binary crossover involves binary numbers. The different types of binary crossover

operations are discussed as under:

Single Point Crossover:

In this type of crossover, a single site is randomly selected along with the span of the

mated strings and bits next to the crossover site are exchanged. The Figure 2.8(a) and

the Figure 2.8 (b) represents parent strings before single point crossover operator as

shown below:

Parent 1

1 1 0 1 0 0 0 1 0 1

Figure 2.8(a): Parent 1 before Single Point Crossover

Parent 2

1 0 1 1 0 0 1 1 0 1

Figure 2.8(b): Parent 2 before Single Point Crossover

After mating of Parent 1 and Parent 2, the result of single point crossover is shown

below in the Figure 2.8(c) and the Figure 2.8(d).

Child 1

1 1 0 1 0 0 1 1 0 1

Figure 2.8(c): Child 1 after Single Point Crossover

Child 2

1 0 1 1 0 0 0 1 0 1

Figure 2.8(d): Child 2 after Single Point Crossover

Double Point Crossover

Two random sites are chosen in this crossover operation. The crossover points in

parent chromosome are at the second and the fifth segments from its bottom strings

before mating with parent 1 and parent 2 are represented using the Figure 2.9(a) and

the Figure 2.9(b) as under.

Crossover Point


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Parent 1

1 1 0 1 0 0 0 1 0 1

Figure 2.9(a): Parent 1 before Double Point Crossover

Parent 2

1 0 1 1 0 0 1 1 0 1

Figure 2.9(b): Parent 2 before Double Point Crossover

After mating of Parent 1 and Parent 2, the result of double point crossover is shown in

the Figure 2.9(c) and the Figure 2.9(d).

Child 1

1 1 0 1 0 0 1 1 0 1

Figure 2.9(c): Child 1 as a Result of Double Point Crossover

Child 2

1 0 1 1 0 0 0 1 0 1

Figure 2.9(d): Child 2 as a Result of Double Point Crossover

Multi Point Crossover

In multipoint crossover operation, the crossover takes place at even and odd

numbered sites. The crossover points are selected according to the length of the string.

The bits lying between alternate pairs of sites are then interchanged [201]. Strings

before mating of parent 1 and parent 2 are represented using the Figure 2.10(a) and

the Figure 2.11(b) as under.

Parent 1

1 1 1 1 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 0

Figure 2.10(a): Parent 1 before Multi Point Crossover

Parent 2

0 0 0 1 0 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0

Figure 2.10(b): Parent 2 before Multipoint Crossover

Crossover Points


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The bits of two strings lying between sites 1 and 2; 3 and 4 and on the right side of 5

are interchanged and the remaining bits are kept unaltered. The generated children are

represented as shown in the Figure 2.10 (c) and the Figure 2.10 (d).

Child 1

1 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 1 1 0 0

Figure 2.10(c): Child 1 after Multi Point Crossover

Child 2

0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0

Figure 2.10(d): Child 2after Multi Point Crossover

Uniform Crossover

This operator is achieved as a result of exchanging bits from two individual parent

chromosomes and maintaining a probability of 0.5 to produce off springs. In the given

example, 2nd

, 4th

, 5th

, 8th

, 9th

, 15th

, 18th

, and 20th

bit positions are selected for swapping

[67]. The Figure 2.11(a) and the Figure 2.12 (b) represent Parent1 and Parent 2 as

under.

Parent 1

1 0 1 1 0 0 0 0 1 1 1 0 0 1 1 0 1 1 1 0

Figure 2.11(a): Parent 1 before Uniform Crossover

Parent 2

0 1 1 1 0 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0

Figure 2.11(b): Parent 2 before Uniform Crossover

Thus, the obtained children solutions are represented as shown in the Figure 2.11(c)

and the Figure 2.11(d). The highlighted colored bits of parent strings are interchanged

in child strings.

Child 1

1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 0

Figure 2.11(c): Child 1 after Uniform Crossover

Child 2

0 0 1 1 0 1 1 0 1 0 1 1 0 0 0 1 0 1 0 0

Figure 2.11(d): Child 2 after Uniform Crossover


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Partially Mapped Crossover

It is proposed by D. E. Goldberg and R. Lingle in 1985 [41] It is designed to prevent

illegal duplication of genes. It is an extension to double point crossover which adopts

a repairing procedure to avoid duplication. The Figure 2.12 (a) and the Figure 2.12 (b)

represent Parent 1 and Parent 2. Two positions of each of the strings are randomly and

uniformly selected and exchanged between parents chromosomes. In case of

duplication in strings is observed, repairing exchanges to be started. As a result of

such swapping, duplicate can be eliminated.

Parent 1

1 2 3 4 5 6 7 8 9

Figure 2.12(a): Parent 1 before Partially Mapped Crossover

Parent 2

4 5 2 1 8 7 6 9 3

Figure 2.12(b): Parent 2 before Partially Mapped Crossover

The children can be generated after mapping relationship as shown in the Figure

2.12(c) and the Figure 2.12 (d):

Child 1

4 2 3 1 8 7 6 5 9

Figure 2.12(c): Child 1 after Partially Mapped Crossover

Child 2

1 8 2 4 5 6 7 9 3

Figure 2.12(d): Child 2 after Partially Mapped Crossover

Matrix Crossover

In the matrix crossover operation, two dimensional array vectors are used. Here, row

and columns of the crossover sites are chosen randomly. The two crossover sites will

form a three layer matrix. One needs to select any region between two layers, either

8 5 7 6 6 7 1 4


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vertically or horizontally and then exchange the information in the region between

mating populations. Example of matrix crossover operation is presented as follows

[154, p.487].

The Figure 2.13 (a) represents Parent1 and Parent 2 strings before mating.

1 0 1

1 0 1

0 1 1

˟

0 0 1

1 0 0

1 1 0

Figure 2.13(a): Parent 1 and Parent 2 before Matrix Crossover

After mating parent 1 and parent 2 using Matrix crossover operator, new generated

off-springs can be represented as shown in Figure 2.13 (b).

0 0 1

1 0 1

0 1 0

1 0 1

1 0 0

1 1 1

Figure 2.13(b): Child1 and Child 2 after Matrix Crossover

Besides these mentioned generalized operators, there are several other operators

available for specific problem categories such as traveling salesperson problem,

machine scheduling, resource allocation, vehicle routing, quadratic assignment

problems, etc.

These operators are enlisted as follows [45, pp. 66-73; 154, pp. 483-499]:

Ordered Crossover

Position Based Crossover

Cycle Crossover

Sub tour exchange Crossover

Heuristic Crossover

Parent 1 Parent 2

Child 1 Child 2


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2. Arithmetical Crossover

In this type of crossover operator, mathematical form is utilized. The weighted

average of two chromosomal vectors x1 and x2 is calculated as αx1+βx2, where α +

β=1, such that α>0 and β>0. There are three types of arithmetic crossover operators,

which are enlisted as under [154, p. 492]:

Convex crossover: It is also called average or intermediate crossover and used

for special case α=β=0.5.

Affine crossover: It is used for specialized case α=1.5 and β= -0.5.

Linear crossover: In the case of linear crossover, the multipliers are strictly

restricted as follows: α + β<=2, where α>0 and β>0.

3. Directional crossover

In this type of crossover operation, problem specific knowledge is introduced into

genetic operations to produce better off-springs.

It uses the following equation:

x'=R x2-x1 +x2

where R is random number between 0 and 1. Here, it is assumed that the parent of x2

is not worse than x1 [154, p. 493].

2.3.5.3 Mutation

Mutations are random alterations in genetic materials. It is designed such a way that

genes are selected randomly and change the allele. Mutation is achieved by flipping

the digits starting from a randomly chosen order. The bit wise mutation performed bit

by bit by changing 0 to 1 and 1 to 0. The purpose of using mutation is to maintain

diversity within the population and inhibit premature convergence [186, p.481].

Example of mutation can be shown in the Figure 2.14(a) and the Figure 2.14 (b).

Parent 1

1 1 0 1 0 0 0 1 0 1

Figure 2.14(a): Parent 1 before Mutation


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As a result of mutation operation, children are generated by flipping single bit or

multiple bits randomly. Child 1 can be represented as shown in Figure 2.14(b) as

under.

Child 1

1 0 0 1 0 1 0 1 1 1

Figure 2.14(b): Child 1 after Mutation

2.4 Work done so far using Genetic Algorithms

As a result of extensive literature survey; it has been observed that GA has been

successfully applied to following research and application areas.

Optimization is the process of finding decisions that satisfy given constraints and

meet a specific goal at its optimal value. The objective of the global optimization is to

find the "best possible" solution in nonlinear decision models that frequently have a

number of sub-optimal (local) solutions. Due to the parallelism that allows GA to

implicitly evaluate many schemas hence, GA can play better role in providing global

optimization [169]. Genetic Algorithms possess important characteristics lacking in

other optimization techniques. A genetic search focuses on a population of points in

contrast to the single point focuses of most search algorithms. The travelling

salesperson problem or variants of it can be solved efficiently using GA. The

problems such as real-world routing of school buses, airlines, delivery trucks and

postal carriers can be modeled as traveling salesperson problem and have been

efficiently solved also [35, p.25].

GAs has been successful in achieving solutions for the variety of scheduling problems

which need to deal with effective distribution of resources. During the scheduling

process many constraints have to be considered [26]. Genetic Algorithm has been also

used to solve the train timetabling problem. The railway scheduling problem

considered in this work implies the optimization of trains on a railway line that is

occupied (or not) by other trains with fixed timetables. The timetable for the new

trains is obtained with a Genetic Algorithm (GA) that includes a guided process to

build the initial population [173].

Genetic Algorithms are designed to play real-time computer strategy games.

Unknown and non-linear search space can be explored using GA and spatial decision


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making strategies and population have been implemented within the individuals of a

Genetic Algorithm [36].

Machine learning approaches to automated knowledge acquisition and re-use in

design through the deployment of a Genetic Algorithm have been implemented that

are operated in conjunction with the problem-solving process. This work presents a

classification of algorithm which is based on Genetic Algorithms and discovers

comprehensible IF-THEN rules in the field of data mining [147].

As a soft computing constituent, GA is capable to be integrated with the other

constituents of soft computing such as neural network, Fuzzy Logic, support vector

machine, etc. The evolving nature of GA has provided useful hybrid architectures for

several applications e.g. diagnosis of cancer, myocardial infarction, rice taste

evaluation, electric power distribution, etc. [166].

Genetic Algorithms (GAs) work with a population of points and hence capable to be

used in multi-objective optimization problems to capture a number of solutions

simultaneously [155]. It is well-known that many decision making problems in

transportation planning and management could be formulated as bi-level

programming models (single-objective or multi-objectives). A genetic-algorithms-

based (GAB) approach is designed to solve the single-objective models as well as

multi-objective problems [83].

In the engineering of mobile telecommunication networks, two major problems can

occur in the design of the network and the frequency assignment. The design of

telecommunication network is of the type of multi-objective constrained

combinatorial optimization problem. In order to achieve this type of optimization, GA

is proposed to increase the speed of the search process; the GA is implemented

parallel on a network of workstations [84].

Graph coloring problem can be easily solved using GA. Generic hyper heuristic

approach for graph coloring has been developed for constructing time table in

examinations and courses [6].

GA has been used to design technical trading rule for analysts and users to do their

research and have advice to buy or sell their shares. This type of application domain is

large and it is difficult to find combination of best parameters. GA is designed such a


46


way that it can find the best rule which is responsible to provide maximum profit

[130].

In order to identify intrusion detection in network, GA is implemented in an unique

way. The exclusive implementation of Genetic Algorithm considers both temporal

and spatial information of the network connections during the encoding of the

problem; therefore, anomalous behavior of network can be identified properly [200].

The Genetic Algorithms have been designed to adjust a classification method as

learning examples-based algorithm, which measures system performance using two

parameters: computing time and accuracy of process. The training phase is

accomplished by the means of two input sets of examples and an adequate fitness

function, to derive the antecedent of the classification rules. An artificial vision

system has been developed to identify defective eggs in the farms [20].

In fashion industry, in order to analyze continuous change of fashion, GA is used as a

design aid system for non professionals. Here, dynamic analysis of data is possible

through evolving nature of GA. An interactive GA has been implemented to classify

three types of designs for women [87].

Genetic Algorithms can be effectively used in the field of software engineering

especially in Object Oriented Software Engineering (OOSE). In order to generate

automated unit test cases in object oriented software testing, GA has been

successfully implemented [153].

Genetic mining for topic based on concept distribution is achieved by implementing

GA. Here, Genetic Algorithm has been developed for text classification,

summarization and information retrieval system in text mining process. This research

showed better performance in text classification with the help of Genetic Algorithm

[185].

2.5 Fuzzy Fundamentals

Fuzzy Logic is a computational model that provides a mathematical way for

representing and manipulating information in a way that resembles human

communication and reasoning processes. Prof L. Zadeh proposed the theory of Fuzzy

Logic (FL) which is designed to handle uncertainty and imprecision. Fuzzy systems

are based on Fuzzy Logic and fuzzy set theory which provide a rich and meaningful


47


addition to standard logic. According to Zadeh [122] degree of knowledge

representation can be enhanced with the use of linguistic variables. Conventional

approach of knowledge representation uses bivalent logic which has major

shortcomings like handling imprecision and uncertainty [166, p.2].

The major reasons behind fuzzy systems development are enlisted as follows [89,

p.547; 174, p.152]:

Fuzzy systems are easy to implement even if the designer has little knowledge

of formal Fuzzy Logic theory.

Fuzzy systems can deal with crucial parameter changes and broadly unstable

load conditions.

Fuzzy Logic is appropriate for industrial processes where the controlled cycle

time may operate over an extended period.

Fuzzy system imitates human reasoning.

Fuzzy system can fulfill need for a mathematical model as well as it is

relatively simple, fast, and adaptive.

With the help of Fuzzy Logic, the mathematically difficult design objectives

can be implemented easily using linguistic or descriptive rules.

In addition to all stated advantages, it has been also observed that FL has the potential

to significantly reduce not only the knowledge acquisition cost but also the

computational cost [171].

2.5.1 Boolean Logic Vs Fuzzy Logic

The classical sets with their operations and properties are useful in expressing

classical logic, which leads to Boolean logic. The classical set is defined by the crisp

boundaries while on the other hand fuzzy set is defined by ambiguous boundaries.

The classical set theory can only deal with bivalent values such as true (1) or false (0)

because the classical set is defined in such a way where the universe of discourse is

divided into two groups: one consists of members and the other consists of non

members. There is no uncertainty about the locations of the set boundaries. The crisp

sets are based on bivalent logic (yes/no) hence, it is not possible to process degrees of

imprecision and uncertainty indicated by words or phrases such as fairly, very, high,

etc. with the help of crisp sets. In order to process such linguistics, there should be

such kind of logic that processes multiple values. FL may be viewed as an extension


48


to classical logic systems, providing an effective framework for dealing with the

problem of knowledge representation in an environment of uncertainty and

imprecision [166, p.2]. The classical logic uses theory of crisp sets while Fuzzy Logic

uses theory of fuzzy sets. Fuzzy sets are based on multi-valued logic. The

aforementioned theories are explained using Figure 2.15(a) and the Figure 2.15(b).

Interestingly, FL furnishes an ability to separate the computational logic from the

fuzziness in data and rules [199]. In conventional binary logic, rules need to be

updated once either logic or fuzziness in data is changed. However, FL revises fuzzy

rules when the logic needs to be changed and adapts membership functions that

characterize the fuzziness when the fuzziness should be changed [143].

2.5.2 Crisp Set

A crisp set is a collection of distinct elements. In the classical set theory, an element

either belongs to a set or does not belong to it [45, p.83]. Suppose, the set A contains

element x using characteristics function µ𝐴 𝑥 is expressed as follows:

µ𝐴 𝑥 = 1, 𝑖𝑓 𝑥 𝑏𝑒𝑙𝑜𝑛𝑔𝑠 𝑡𝑜 𝐴;

= 0, 𝑖𝑓 𝑥 𝑑𝑜𝑒𝑠 𝑛𝑜𝑡 𝑏𝑒𝑙𝑜𝑛𝑔 𝑡𝑜 𝐴 .

The characteristic function µA(x) in a classical set theory can only state that element

does belong to a set or it does not belong to it, e.g. temperature is either cold or hot. It

can distinguish between only two values: 1 (true) and 0 (false).

2.5.3 Fuzzy Sets

Fuzzy sets are such type of sets that may not posses either clear or crisp boundaries.

They represent vague boundaries. Fuzzy sets are the clever ways to deal with

vagueness as we often do in our daily life because the elements contained in the fuzzy

sets have a partial degree of membership. In a fuzzy set, an element can be a member

of the set with some membership value or degree of belongingness. A number

0 0.

2 0.6 0.4

4

0.

8

1

0 0 0 1 1 1

Figure 2.15(a): Boolean Logic Figure 2.15(b): Fuzzy Logic


49


between 0 and 1 indicates the degree of membership to a set in the interval [0, 1]. The

definition of fuzzy set is represented as follows:

Fuzzy set, defined as A, is a subset of a universe of discourse U, where A is

characterized by a membership function µ𝐴 𝑥 .

µ𝐴 𝑥 : X 0, 1 , where µ𝐴 𝑥 = 1 if x is totally in A;

= 0 if x is not in A;

= 0 < μA < 1 𝑖𝑓 𝑥 𝑖𝑛 𝑝𝑎𝑟𝑡𝑙𝑦 𝑖𝑛 𝐴.

Mathematically, fuzzy sets are more complicated than classical sets but they provide

more natural representation of the real world entities. Fuzzy set has the following

properties:

It has smooth boundary.

Basically, membership in a set is degree or degree of truth- ness.

In the fuzzy set theory, belongingness is a matter of degree (depends upon grade of

membership) e.g temperature can be high and low or medium according to the range

of values.

2.6 Membership Functions

Any set can be represented by mathematical function. In a classical set theory, the

characteristic function, denoted by µA(x) is used to represent a crisp set while with

reference to the fuzzy set theory the characteristic function is known as membership

function denoted by µA(x). A fuzzy set may be represented using this membership

function. This function gives the grade (degree of membership) within the set of any

element of the universe of discourse. The membership functions are used to process

numeric input data.

A fuzzy set A(x) is represented by a pair of two things- the element x and its

membership value µA(x). This representation is shown using following Equation.

A(x)={(x, µA(x)), x belongs to X; µA(x) belongs to [0, 1]} where X is (universe of

discourse).


50


The membership function maps the elements of the universe to numerical values in

the interval [0, 1]. Thus, the membership function can take intermediate values

between 1 and 0 and is indicated by [0, 1].

A membership function (MF or μ) is a curve that defines how each point in the input

space is mapped to a membership value (or degree of membership) between 0 and 1.

Thus shape, the overlapping, peak values, and their continuity properties decide how

the fuzzy system can be designed and how it behaves. The input space is sometimes

referred to as the universe of discourse.

Membership functions can be better understood through the example shown in Figure

2.16. The example of identifying color of strawberry is presented using crisp set and

fuzzy set theory [45, p.83] as discussed below.

Figure 2.16: Example of Membership of Crisp set and Fuzzy Set

According to the crisp set theory, color of strawberry is either red (1) or non- red (0).

In such case, value of membership µ is either 1 or 0. According to the fuzzy set,

membership values are defined as CR-Completely Red, AR-Almost Red, MR-Minor

Red and NR-No Red. If color is Completely Red (CR) then, membership value µ is

1.0, if color is Almost Red (AR) then µ=0.7, if color is MR then µ=0.4 and if color is

NR then, µ=0.0.

A fuzzy set can be represented in two ways as under:

Crisp Set Fuzzy Set

If member =

no, µ=0.0

Is the Strawberry red?

If member = yes,

µ=1.0

If CR,

µ=1.0

If AR,

µ=0.7

If NR,

µ=0.0

CR: Completely

Red

AR: Almost Red

MR: Minor Red

NR: No Red

If MR,

µ=0.0


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By enumerating membership value of those elements in the set completely or

partially. A fuzzy set A can be defined by enumeration using expression as shown

below.

µ(xi)

xi

where µ(xi) refers to fuzzy set containing exactly one (partial) element x with

membership degree µi(xi).

By defining the membership function mathematically, e.g. The term High in terms

of Temperature (T) can be defined mathematically as under.

High = 0, if 0 < T < 30;

= T − 30 100 , if 30 < T < 100;

= 1, if T > 100;

In the classical set theory, every individual object is assigned a membership value

either 1 or 0 that discriminates between membership and non membership of the crisp

set whereas in the fuzzy set an object can take a grade of membership between 0 and

1 i.e. [0 1].

There are various types of membership functions distributions are in use, some of

them are discussed as under [45, pp. 84-87].

Triangular Membership Function

Figure 2.17 represents Triangular Membership Function. It is denoted by Triangle(x;

a, b, c). The membership function value 𝜇𝑡𝑟𝑖𝑎𝑛𝑔𝑙𝑒 for this distribution is determined

as under.

𝜇𝑇𝑟𝑖𝑎𝑛𝑔𝑙𝑒 = 𝑚𝑎𝑥 𝑚𝑖𝑛 𝑥 − 𝑎

𝑏 − 𝑎 ,

𝑐 − 𝑥

𝑐 − 𝑏 , 0

𝜇𝑇𝑟𝑖𝑎𝑛𝑔𝑙𝑒 values are set to 0.0, 1.0 and 0.0 at x=a, x=b and x=c respectively.


52


Figure 2.17: Triangular Membership Function

Trapezoidal Membership Function

Figure 2.18 shows trapezoidal membership function distribution Trapezoidal (x; a, b,

c, d). The membership values can be expressed like the following Equation.

𝜇𝑇𝑟𝑎𝑝𝑒𝑧𝑜𝑑𝑖𝑎𝑙 = 𝑚𝑎𝑥 𝑚𝑖𝑛 𝑥 − 𝑎

𝑏 − 𝑎 ,1,

𝑑 − 𝑥

𝑑 − 𝑐 , 0

The membership function values at x=a, x=b, x=c and x=d are set to 0.0,1.0 ,1.0 and

0.0 respectively.

Figure 2:18: Trapezoidal Membership Function

Gaussian Membership Function

The gaussian membership function distribution is shown in the Figure 2.19 and

indicated by Gaussian (x; m, σ) where m and σ represent the mean and standard

deviation of the distribution respectively. The membership function is represented as

under.


53


𝜇𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛 =1

𝑒1

2 𝑥−𝑚

𝜎2 2

Figure 2:19: Gaussian Membership Function

at this point x=m, the value of 𝜇𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛 comes out to be equal to 1.0 and as the

value of x deviates more and more from m, the value of 𝜇𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛 tends towards 0.0.

Bell shaped Membership Function

The bell shaped membership function distribution is represented in the Figure 2.20 as

Bell Shaped (x; a, b, c) and it is expressed as under:

𝜇𝐵𝑒𝑙𝑙 −𝑆𝑕𝑎𝑝𝑒𝑑 =1

1 + 𝑥−𝑐

𝑎

2𝑏

Where „a‟ controls the width of the function, „b‟ is a positive number which indicates

the slop of the distribution and „c‟ is the center of the function.

Figure 2.20: Bell Shaped Membership Function


54


Sigmoid Membership Function

The sigmoid membership function is represented in the Figure 2.21 as sigmoid(x; a,

b) and its membership function 𝜇𝑠𝑖𝑔𝑚𝑜𝑖𝑑 is expressed as under.

𝜇𝑠𝑖𝑔𝑚𝑜𝑖𝑑 =1

1 + 𝑒−𝑎 𝑥−𝑏

Thus 𝜇𝑠𝑖𝑔𝑚𝑜𝑖𝑑 becomes equal to 0.5 at x=b. It tends to 0.0 and 1.0 as x approaches to

0.0 and value higher than „b‟ respectively.

Figure 2.21: Sigmoid Membership Function

Apart from the above stated typical membership functions, it is also possible to design

variations of such functions. According to Jantzen [103], the fuzzy set theory suggests

that there is not any practiced method for determining the shape and width of a fuzzy

membership function. It is a subjective process that will vary with the designer of the

control system.

2.7 Linguistic Variables and Rule Bases

The fuzzy system employs linguistic descriptors rather than absolute numerical

values. Linguistic terms are used to express concepts and knowledge in human

communication. A linguistic variable is a composition of symbols and numbers [107,

p.55]. According to Prof. L. Zadeh, degree of knowledge representation can be

enhanced with the use of linguistic variables [122]. Values of the linguistic variables

are defined by the context dependant fuzzy sets whose meanings are specified by

gradual membership functions. A linguistic variable V is characterized by following

variables:

a “name” is represented by x,

a “universe‟ is represented by U,

a “term set‟ is represented by T(x),


55


a “syntactic rule” is represented by G for generating names of values of x, and

a “semantic rule” is represented by M for associating meanings with values.

For example: The speed of a car can be described using linguistic variables. The

Figure 2.22 represents multiple linguistic variables for deciding the categories of

speed as shown below.

name x = Speed,

universe U= [0:220] (possible crisp values),

term set T(x) = {Very slow, Slow, Average, Fast, Very Fast},

syntactic rule G: a fuzzy set for each term from T(x),

semantic rule M: an interpretation for each term from T(x), and

The value of a linguistic variable can be qualitatively described by a linguistic term

and quantitatively described by corresponding membership function which conveys

the meaning of fuzzy set [154, p.363].

Figure 2.22: Representation of Linguistic Variable “Speed”

2.8 Fuzzy Rule Based Systems

A fuzzy system (FS) is any FL based system, which either uses FL as the basis for

knowledge representation using different forms of knowledge or models the

interaction and relationships among the system variables[166, p.1]. There are two

major categories of fuzzy systems: Fuzzy Controller and Fuzzy Rule Based Systems.

The application of FL to rule based systems leads to concepts of Fuzzy Rule Based


56


Systems (FRBSs). Fuzzy rule based systems are an extension of classical rule based

systems. Due to efficient handling of uncertainty, such systems become prominent

constituents of the soft computing. Fuzzy systems have demonstrated their ability to

solve different kinds of problems in various application domains. One of the most

popular fuzzy systems is Fuzzy Rule Based Systems (FRBS); which have 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. Fuzzy

systems are popular as they are able to solve non-linear control problem, reveal robust

behavior and inexpensive to implement. Designers are especially attracted to fuzzy

systems because fuzzy systems allow them to capture domain knowledge quickly

using rules that contain fuzzy linguistic terms. Fuzzy systems can also provide cost

effectiveness and high performance [138]. It is necessary to insert knowledge for

designing rule based expert system.

Knowledge can be represented in the simplest form by using classification rules

which are popularly known as “if- then rules”. The set of rules represents knowledge

about the domain which will form knowledgebase. Systems employing such rules as

the major representation paradigm are called rule based systems. Rule based system

provide significant advantages such as modeling of a system which resemblance as

human expert and competent problem solving behavior.

The general form of a rule is presented as under.

If Cond. 1 and Cond. 2 and Cond. 3 … then Action1, Action2, . . .

The antecedent part of the rule consists of conditions Cond.1, Cond. 2, Cond.3, etc.

These parts are evaluated based on the contents of the working memory while

consequent part consists of actions action1, action 2, etc. Usually, the condition of a

rule is a predicate in certain logic, and the action is an associated class i.e. prediction

of action for an input instance is only possible if condition becomes true. Such rules

are interpreted to mean that if the antecedents of the rule together evaluate to true.

Each antecedent of a rule typically checks if the particular problem instance satisfies

some conditions.

A fuzzy expert system is an expert system that uses a collection of fuzzy membership

functions and rules, instead of Boolean logic, to reason about data. Sometimes the

knowledge which is expressed in the form of rules is uncertain [143, p.317]. In such


57


cases, typically, a degree of certainty is attached to the rules. This type of knowledge

is considered as fuzzy knowledge i.e. rules in a fuzzy expert system are usually of a

form similar to the following:

If A is low and B is high then X is medium;

where A and B are input variables and X is output variable.

Here, low, high, and medium are fuzzy sets defined on A, B, and X respectively. The

antecedent describes at what degree the rule is applied, while the rule‟s consequent

assigns a membership function to each of one or more output variables.

Fuzzy systems exhibit, the following characteristics that render them adequately to

solve the problem tackled in this thesis.

The knowledge representation is possible in a human understandable way using

the linguistic rules to explain decision processes. They express concepts with

linguistic labels, close to human representation (e.g., “high fever” instead of

“temperature higher than 39.3 degrees”).

Such linguistic representation (i.e., concepts and rules) is accompanied by a

precise numeric equivalent that is adequate for managing information available in

a numeric way.

2.9 Work done so far using Fuzzy Logic based systems

Over the past two decades, there has been tremendous growth in the utilization of

Fuzzy Logic in control systems as well as in decision making systems. Various

industrial and commercial products and systems have been utilizing FL based

systems. In several applications related to non linear, time-varying, ill-defined

systems and also complex systems, the fuzzy systems have been proven very efficient.

The major applications of FRBSs are fuzzy modeling, fuzzy control and fuzzy

classification. Some representative applications are mentioned as under.

Linguistic modeling has been designed in order to predict the exchange rate to support

decision making in currency trading [91,189]. In international economics forecasting,

explicating the behavior of nominal exchange rates has been a prime theme in

economists' work due to execution of modeling exchange rates which are by nature

notoriously challenging task. Fuzzy Logic has been utilized in order to further


58


enhance the predictive accuracy of financial forecasting of time series for predicting

exchange rates of Taiwan and US dollar [175].

In the field of weather forecasting, fuzzy modeling has been effectively implemented.

Modeling of daily mean temperature for weather forecasting using Fuzzy Logic is

achieved. Classification of daily atmospheric circulation is capable to achieve various

meteorological variables such as the local or regional daily precipitation, temperature

and wind [3].

In the past decade, Fuzzy Logic has been proved to be wonderful tool for intelligent

systems in medicine. There are several Fuzzy Logic based applications developed so

far in the medical domain. Diagnostic systems have been taking advantages of Fuzzy

Logic in order to handle imprecise information. Particularly rule based systems are

proven more successful for diagnostic systems. Several popular applications are

illustrated as under:

Application of Fuzzy Logic in developing rule based system for diagnosis of lung

diseases is named as DoctorMoon. DoctorMoon records all the reasoning steps:

getting patient‟s symptoms, matching rules, diagnosing, etc., for generating a report

when the diagnosis has been done [152].

An FRBS has been developed to estimate human dental age from tooth eruption status

and patient‟s chronological age. This system is particularly useful for orthodontists

and pediatric dentists so that they can suggest suitable treatment in order to prevent

dental problems [138].

In order to design applications of segmentation of geographic and satellite map

images, fuzzy modeling has been implemented effectively. Segmentation of grey

scale geographic maps into foreground regions is made possible with classification

[204].

Subjective qualification of food taste constitutes an important and complex problem.

The quality of rice is evaluated by group of 24 experts that performs a subjective

sensory test. Modeling the rice quality evaluation becomes a complex problem due to

large number of attributes. The accurate and user interpretable model is designed with

linguistic modeling [77].

A variety of linguistic and fuzzy models are designed to solve two major kinds of

electrical distribution problems such as estimation of low voltage line installed in


59


villages and computing of the maintanance costs of medium voltage lines in town

[132,164]. Such type of electrical distribution problems have also been solved by

another approach for Rule Base learning. This approach implements adhoc data

driven linguistic and fuzzy rule learning methods in which new rules can be directly

generated from the training data based on covering criterion [96].

Sensor based fuzzy contol systems have been designed in industrial automation and

consumer products such as fuzzy control of water treatment plant, fuzzy automatic

washing machine,automatated train driving,applications of behaviour based

robotics,etc.[166, pp.40].

Fuzzy Logic provides an approximate yet practical means of knowledge processing.

Sensor based applications for domestic usages are proven successful for various

activities handling uncertainty or imprecise characteristics of the real life situations.

Fuzzy Logic has also been utilized to develop fuzzy intelligent systems such as fuzzy

washing machines, fuzzy cameras and camcorders [154, pp.330-331].

In the education field, in order to achieve performance evaluation of students, an

approach based on fuzzy information processing has been implemented. This

approach utilizes data driven fuzzy rule induction method to identify students‟

academic performance [112].

Besides, all the above stated specific applications, several non-sensor based

applications have also utilized various advantages of the characteristics of Fuzzy

Logic.

2.10 Role of Education in Human Life

The growth of every individual depends on many factors; major of them can be

enlisted as under:

Personal attitude;

Social awareness with responsibilities;

Understanding and learning capability;

Educational environment;

Technological facilities;

Industrial support; and

Economical conditions and many more.


60


Out of the above factors, education is one of the prime fields responsible to make an

individual stronger with professional capabilities. Education is a productive and

beneficial factor in a person‟s life. It is everyone‟s right to get education. The training

of a human mind is not complete without education. With the help of education, a

man is able to receive information from the external humanity to notify him with past

and receive all essential information concerning the present. The aim of true

education is certainly not to load the memory with knowledge, but to "draw out" or

build up the capabilities of the mind. The major goal of education is to provide

significant knowledge to each and every individual to progress in all areas.

An appropriate education helps people to learn skills and knowledge they will need in

their careers. Knowledge grows in accordance with needs and conditions of a society.

Education helps people how to think, how to work properly, how to make decision.

Through education only, one can make separate identity which is a prime need of

today‟s professional life. In order to achieve success, educational methods are

required to be implemented properly. And as a result of such implementation, a

human being can become capable to achieve success.

Computer and technological advancements have been contributing to individual‟s life

in almost all areas. Technological advancements increase the speed of problem

solving and finally decision-making; however, the basic requirement is to develop

problem solving ability in human being. Basically, intelligence is such an ability

which is genetically achieved by every human being in different capacities. In order to

develop problem solving skills, it is desired to utilize intelligence as per the

requirements of circumstances. It is obvious that there is a tight coupling between

intelligence and education.

2.11 Role of Intelligence for Success

Intelligence can also be described as “the capacity to learn and understand”.

Intelligence is an ability to handle complexity and solve problems in some useful

context. To deal with real life problems, a certain level of intelligence is essential for

every individual. Genetically, every human being acquires intelligence that helps

them in solving the problems throughout the life. This problem solving ability of a

person can be developed as well as enhanced with the help of proper educational

methods from childhood and during developmental life cycle. The results of many


61


researchers have shown that appropriate training and development methods can

increase the level of intelligence by utilizing instructional technologies. ICT and

education field together have enhanced skills of individuals and help them in

developing problem solving ability [118]. This ability can serve as the potential bases

for professional success.

Figure 2.23 shows the role of intelligence as a base for professional success.

Figure 2:23: Intelligence as a Base for Professional Success

Intelligence is a combination of five abilities i.e. perception, information processing,

memory, learning and behavior. The way in which intelligence is utilized in reality is

known as modes of intelligence. The Figure 2.24 shows different modes of

intelligence observed by several research projects [180]. These modes are explained

in detail as under.

Existential Intelligence

An authentic and flexible engagement with the demands of the environment -

sometimes called Requisite Variety. This mode is the basis mode of human

intelligence as every human being must have existential intelligence in different

capacities. According to the strength of existential intelligence, different intelligences

of human being can be enhanced.

Intelligence

Educational Qualification

Skill Development

Problem Solving

Capability

Decision

Making


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Business Intelligence

Business Intelligence is defined as capability to actively collect, interpret and utilize

vast quantities of complex data. In order to achieve this intelligence, varieties of

business rule are implemented along with policies, structural constraints and process

constraints. This quality is finally responsible for flexible system design along with

system understanding and system inference.

Figure 2.24: Modes of Intelligence

Organizational Intelligence

An organization is a socio-technical system and is composed of many interoperating

systems which involve two major actors: employees and machines. Organizations

contain many pieces of intelligence in form of these above stated two actors. Human

intelligence of many employees is combined with the artificial intelligence of

machines, contained in intelligent buildings, and distributed through intelligent

cyberspace collectively. In order to make an intelligent organization, it isn't enough to

employ the highly knowledgeable people but also it is essential to locate them in

state-of-the-art office buildings, and provide them with the smartest computer tools

and networks. Still, super-intelligent individuals are often poor at talking to one

another and sharing knowledge [180]. They are to be coordinated effectively and

finally tasks and opportunities can be handled collaboratively. As a result of such

stated activities of an organization, it is capable to work as an intelligent organization.

Developmental Intelligence

Developmental Intelligence is defined as the capacity to acquire and use knowledge

effectively for personal and organizational learning. Intelligence can be divided into

five abilities enlisted as: perception, information processing, memory, learning and

Existential

Intelligence

Business

Intelligence

Developmental

Intelligence

Organizational

Intelligence


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behavior. Traditional educational system had focused on developing memory but

today‟s educational system heavily focuses on information processing skill which is a

very important skill in today‟s world. But a balanced education must pay attention to

all five abilities of intelligence [180].

The focus of this research work is on the developmental mode of intelligence by

providing classification of human capabilities. This mode provides the direction to

work with the designs of various support systems to deal with education, human

resources, production and maintenance, research and development projects for

individuals as well as organization to increase the socio-economic values.

2.12 Need of Multiple Intelligences

There are several theories developed to understand human intelligence. According to

conventional approach, every human being has a single intelligence, which is often

called general intelligence. Intelligence is an ability of human being to do something

which could be assessed and measured in respect of each individual. Such

measurement could be used to make decisions on the suitability of individuals for

particular contexts, such as education or work. Every individual is genetically blessed

with certain intelligence which is difficult to be changed. Psychologists can evaluate

one's intelligence (IQ) by utilizing short-answer tests and other measures such as the

time it takes to react to a flashing light or the presence of a certain pattern of brain

waves [76]. But such conventional IQ tests were not capable of convincing the

researchers. So, later on several alternative theories have been developed to identify

human intelligence. As a result, it has been observed that intelligence is the result of a

number of independent abilities that uniquely contribute to human performance.

These theories suggest that human intelligence is modifiable, comprehensive and

proficient of development rather than being fixed, unitary, and pre-determined [64,

72, 133, 178].

As a result of extensive research work of cognitive science on human intelligence, it

has been observed that there are two major paradigms of intelligence exist in human

being [19, 37, 72, 81, 178]:

1. General intelligence, and

2. Multiple Intelligences.


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Currently, much of the modern research on intelligence, are more concerned with the

processes of intelligent thinking with the organizational traits that define it. Since the

traditional intelligences have always focused on rather narrowly cognitive ability on

particular tasks rather than on the patterns of performance abilities across tasks [42].

Because of such characteristics, the usages of general theories of intelligence have

been relatively rare and infrequent. Although, Gardner‟s theory of Multiple

Intelligence has attracted considerable and popular attention, it has been concluded

that intelligence is not unitary but is exhibited in multiple ways as summarized in

Table 2.1[88].

Table 2.1: Comparison of Old View and New View of Human Intelligence

Old View of Intelligence New View of Intelligence

Intelligence was fixed. Intelligence can be developed.

Intelligence was unitary. Intelligence can be exhibited in many ways-

multiple intelligences.

Intelligence was measured

by score number.

Quantitative analysis of intelligence is not possible

but it is exhibited during performance or problem

solving process.

Intelligence was measured

in isolation.

Intelligence is measured in context/real life

situations.

Intelligence was used to

sort human beings and

predict their success.

Intelligence is used to understand human capacities

and there are many and varied ways available for

human beings to recognize their capabilities.

As an outcome of the literature survey, there are several popular definitions of human

intelligence; few are defined as follows:

“The ability to solve problems or to create products that is valued within one

or more cultural settings” [71, p.14].

“A bio-psychological potential to process information that can be activated in

a cultural setting to solve problems or create products that are of value in a

culture” [75, pp. 33-34].

“Intelligence is the capacity to learn from experience, using meta-cognitive

processes to enhance learning, and the ability to adapt to the surrounding

environment, which may require different adaptations within different social

and cultural context” [178, p.486].


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Gardner tried to propose a theory with multiple intelligences also because the current

psychometric tests examined only the linguistic, logical, and some aspects of spatial

intelligence, whereas the other intellectual abilities such as athleticism, musical talent,

and social awareness were ignored by those tests. In addition to psychometric data,

Gardner takes into account numerous sources of interdisciplinary evidence from

developmental psychology, neuropsychology, biology, and anthropology in

formulating his theory [146]. Thus, the MI theory has the potential to make science

accessible to all the students because it acknowledges each student's unique cognitive

profile [73]. Gardner, in his MI theory, proposes that human intelligence has multiple

dimensions that must be recognized and developed in education. He notes that

traditional IQ or intelligence tests measure only logic and language, but there are

other equally important types of intelligence [95, p.115].

Once this broader and more realistic perspective of human life was considered, then

the concept of intelligence began to lose its magnetism and became a functional

concept that could be seen working in people‟s lives in a variety of ways. Gardner

provided a means of mapping the broad range of abilities that humans possess by

grouping their capabilities into the nine comprehensive categories or “intelligences”

as shown in Table 2.2.

As a result of several researches in the area of human intelligence, it has been

observed that human intelligence is not limited to only 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 [114]. So it is obvious

that level of different types of intelligence is different in every human being.

2.13 Types of Multiple Intelligences

There are various theories invented by many researchers to identify the types of

intelligence in the human beings. Gardner‟s Theory of Multiple Intelligences (MI)

proposes a means to understanding the many ways in which human beings are

intelligent; that is, how we process, learn, and remember information, in contrast to

the customary notions of intelligence testing, which hypothesize general intelligence.

In initial model of MI, there were seven types found by Gardner [71]. But in (1993),

Gardner has identified two more intelligences Moral and Existential [72]. It is


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observed that there is also a possibility of many other types of intelligence exist in

individuals, eg. Theory of MI has supported to identify meta-intelligence named as

“Digital Intelligence” [151].

MI theory is a definition and conceptualization of human intelligence. It neither

prescribes a particular approach nor a set of activities. Table 2.2 describes the various

types of intelligence along with their meanings [113].

Table 2.2: Set of Multiple Intelligences based on Gardner’s Theory

Type of Intelligence Meaning

Linguistic/Verbal

Intelligence

The capacity to learn, understand and express using

languages e.g. formal speech, verbal debate, creative

writing, etc.

Logical-

Mathematical

Intelligence

The capacity to learn and solve problems using

mathematics e.g. numerical aptitude, problem solving,

deciphering codes, etc.

Spatial/Visual

Intelligence

The ability to represent the spatial world of mind using

some images e.g. patterns and designs, painting,

imagination, sculpturing, etc.

Bodily-Kinesthetic

Intelligence

The capacity of using whole body or some parts to solve a

problem e.g. body language, creative dance, physical

exercise, drama, etc.

Musical

Intelligence

The capacity to understand music, to be able to hear

patterns, recognizes them and perhaps manipulates them

e.g. music performance, singing, musical composition, etc.

Interpersonal

Intelligence

The ability to understand other people e.g. person-to-

person communication, group projects, collaboration

skills, etc.

Intrapersonal

Intelligence

The ability to understand personality aspect e.g. emotional

processing, knowing himself, etc.

Naturalist

Intelligence

The ability to discriminate among living things and

sensitivity towards natural world e.g. knowledge and

classification of plants and animals with naturalistic

attitude, etc.

Existential and

Moral Intelligence

It concerns with ultimate issues as well as capable of

changing attitude. It is said to be required with every

individual.

Each individual varies in the degree of skill possessed in each of these intelligences.

The Theory of Multiple Intelligence focuses on following:

Every individual can be educated by utilizing personal computers.


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Students can be made to learn through implementation of different ideas and

concepts in different formats according to their interest which can make

certain creations of multiple intelligences among them.

The key to understand multiple intelligence theory is to understand that each person

has strengths and weaknesses in each of above stated types of intelligence. These

intelligences are dynamic in nature rather than static, that is, they are capable of

changing over time.

2.14 Work done So Far using Theory of MI

The theory of MI and its implementations have been employed to various fields of

education. In conjunction, both multiple intelligences and learning styles can work

together to form a powerful and integrated model of human intelligence and learning

style [88]. In 1996, the significance of integration of multiple intelligences and

learning strategies has been proposed by Felder & Soloman[179]. In his research

studies of 1997, V. B. Emig showed that students learned in different ways and this

result was found most important part of Multiple Intelligences Theory [196].

Educators have become more aware of the research that has been done by cognitive

and educational psychologists in the area of learning styles and multiple intelligences

[177].

In particular, a number of articles have explored the possibility of applying multiple

intelligences to the teaching of English to grade school students [82, 87] This research

has shown that the applications of MI theory had a positive influence on learning

English in class and enhanced students‟ interest in language learning. MI can be used

to improve learning style to enhance language instruction [24, 39]. During the class

sessions, when students are required to answer questions or to read out assigned notes,

students who are weak in verbal-linguistic and logical-mathematical intelligences are

neglected unfortunately. Many studies suggest that traditional teaching practices do

not promote achievements and attitudes toward science.

Due to limitations of traditional teaching stated above, those students who are strong

in verbal, linguistic and logical-mathematical intelligences have not taken advantages

of their capabilities [106]. In comparison with traditional teaching approach, the MI

teaching approach combines intelligences with creative ways to address the

uniqueness of the individual learners [191]. MI teaching approach may encourage


68


students‟ achievement and affective dispositions in science [168]. In order to facilitate

effective learning, teacher can design teaching material based on the theory of

multiple intelligences which suggests different types of instructions an effective way

instead of more traditional linguistic or logical ways of instruction. For example, to

teach or learn about the law of supply and demand in economics, you might read

about it (linguistic), study mathematical formulas that express it (logical-

mathematical), examine a graphic chart that illustrates the principle (spatial), observe

the law in the natural world (naturalist) or in the human world of commerce

(interpersonal), etc.

Theory of MI has been implemented to provide a planning tool for curriculum

differentiation. The tool has been implemented through learning centers in small

elementary schools [193]. In 2005, M. A. Carvalho et al. proposed that MI has been

used to construct virtual reality environment which is also known as image tic

scenarios. Here, an analysis and a verification tool have been designed using MI in

order to communicate leadership vision [134].

A reseach has been implemented using multimedia-assisted call instruction. Kim has

presented a study comparing students‟ learning preferences, obtained through an MI

inventory survey, to their listening scores before and after call instruction. The survey

findings show how language learning software could be implemented to increase

students‟ use of multiple learning styles [94].

Educational social software becomes popular due to their technical capabilities for

providing communication and interaction among users. In order to find significance of

interpersonal, intrapersonal and linguistics intelligence types on activities of blog, a

survey was conducted by [62].

The development of an interactive, comprehensive and multidimensional educational

courseware as an effective learning tool to assist educators in teaching and learning

process has been made possible for Malaysia Smart School which is self directed, self

pace and self access for multiple styles of learning by [92].

It is also observed that Multiple Intelligences Theory has been implemented on career

development. Multiple intelligences can be related with potential career choices and

the identification of MI strengths and weaknesses might help all students to identify

the careers they are best suited to pursue [119]. In 2003, in a study of G.Yılmaz, and


69


S. Fer, it has been also expressed that students can be guided to achieve careers that

suit their dominant MI. There were certain professions that were more favorable to

certain types of MI [69]. Although, certain professions have been matched with

certain intelligences, only a few limited research studies have been found in literature.

A study on program development process has been made to find students‟ learning

ability to determine 1st, 2

nd and 3

rd grade children‟s multiple intelligence profiles. The

aim was to measure problems and challenges of students related to learning. This

study has provided guidance to educators to analyze their own multiple intelligences

as well as positive contribution to the field of curriculum and instruction [156, p.98].

There are two major tools available for the assessment of learner‟s multiple

intelligences namely Multiple Intelligence Developmental Assessment Scales

(MIDAS) developed by Shearer Brandon [181] in 1997 and Teele Inventory of

Multiple Intelligences (TIMI) developed by Sue Teele [189].

A study was conducted on the online learning with the Theory of Multiple

Intelligence which demonstrates the feasibility of building a personalized online

learning environment. The prototype developed for hypermedia online learning which

forms the basis for an “anyone, anyhow” approach for online learning, seeking to

eliminate constraints due to unstable development of intellectual faculties through the

use of educational methodologies [23].

Adult development programs have been organized to remove negative self image

from adults and to develop self confidence to academic success. A study has been

conducted on utilizing MI based instructions in order to effectively develop adult

literacy [105].

In order to assess the level of MI among young adolescents and to understand the sex

differences in the level measured of multiple intelligence, a study was conducted. It

has been observed that girls have shown better performances in case of linguistic and

musical intelligence whereas boys have shown lead performances in logical and

bodily kinesthetic intelligence [65].

The research project “EDUCE” proposed by Kelly, in [44] has been implemented as a

predictive system using MI. As an outcome of this research project, a predictive

engine was developed in order to dynamically diagnose the MI profile from the


70


student‟s behavior. Using this profile, it becomes possible to make predictions on

what MI informed resource the learner prefers and does not prefer.

The study conducted by O. N. Kaya, in 2006 is to help especially pre service and in

service science teachers for developing and implementing an MI science lesson. A

case study, how a science lesson was planned and practiced in an 8th grade classroom

was qualitatively explained in the research report [168].

The intellectual capability of human being can be developed to different degrees.

Thus, it is highly desirable that teaching techniques are used to stimulate the greatest

possible number of intelligences. Digital Systems heavily focus on logical reasoning

while stimulating intelligences other than those integrally related to the discipline

under study turns out to be a challenging task. An application of the Theory of MI

has been designed to Digital Systems Teaching [29]. In 2010, Hernandez et.al have

presented a study on knowledge generation and innovation, linking, finance and

comprehensive assessment of the educational process using Theory of MI in Mexico

[197].

In 2012, Mark Pearson has provided a framework which is the result of integration of

MI Theory into the field of counseling for integrative counselor education. This

framework can provide more flexible delivery of services to clients and utilize new

ways to develop a broad theoretical approach to strengthen counselor [145]. A study

presented on application of the Theory of Multiple Intelligences in Arts education.

Here, it is observed that there is a significant difference both types of instruction:

traditionally designed instruction and MI based instruction. The MI based education

provides greater achievement in increasing the levels at knowledge, grade, a sense of

art, interpretation, remembrance and an aesthetic value [192].

As a result of such review, it has been observed that there are several projects

designed and implemented using information technology. Still, the Theory of MI has

not been utilized to classify different intelligences for professional success using

evolutionary-fuzzy approaches. Hence, a model is proposed for classifying multiple

intelligences using evolving knowledgebase approach. The problem is to suggest the

suitable career field for the user according to different types of intelligence user

possess. In order to have prediction for an appropriate career field for the user, the

evolutionary fuzzy model is designed.


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2.15 Conclusion

Having classified into three major sections, this chapter initiates with explaining the

role of evolutionary algorithm for search and optimization by utilizing various

characteristics of evolution cycle. Among several constituents of EA, GA becomes

the prime component due to providing significant characteristics such as parallelism

as well as optimization. Genetic Algorithms are computerized search and optimization

algorithms based on the mechanics of natural genetics and natural selection. The life

cycle of GA is presented along with significance of encoding schemes, fitness

function as well as genetic operators. Genetic operators are responsible for

incorporating different combinations of parent generations which results into child

generations. The chapter also presents numerous applications designed in order to

achieve search and optimization in different domains. This includes scheduling,

machine learning, aircraft designing, transportation, fashion design, and many more.

The literature review has shown that till now, GA has been extensively implemented

to achieve optimization in engineering applications, but GA has not been

implemented widely in education domain for achieving optimized rule learning. The

main characteristic of GA is that it can be easily integrated with the other soft

computing constituent but GA lacks imprecision and linguistic knowledge

representation. This limitation can be overcome by integrating GA with FL as FL is a

powerful candidate of SC for hybridization. The chapter presents fundamentals of

Fuzzy Logic along with comparison of bivalent logic. The linguistic knowledge

representation is made possible in the human understandable format using Fuzzy

Logic. Fuzzy Logic is explained by designing specialized membership functions such

Triangular, Trapezoidal, Gaussian, etc. These characteristics have made FL popular in

real life application dealing with imprecision and uncertainty. The chapter also

presents useful applications based on fuzzy modeling in various domains, etc. The

review leads to conclusion that integration of FL with GA provides best possible

outcome to handle imprecision in real life application which is not possible with

traditional logic.

In order to utilize the significant characteristics of GA and FL, an application is

required to be designed based on this stated approaches. The third section deals with

an application domain of the theory of MI and initiates with an explanation of the

factors affecting growth of human beings. Being, one of the most significant factors,


72


the role of education is integrated into the chapter. Intelligence is justified as a base

for professional success which is classified among several constituents such as

educational qualification, skill development, problem solving ability as well as

decision making skills. The way of utilizing intelligence is known as mode of

intelligence. Among four modes of intelligence, the research application focuses on

the developmental mode which is the ability of human being to acquire and use

knowledge. The chapter presents literature review on human intelligence by

presenting traditional and modern view of human intelligence and narrates advantages

of modern view of intelligence. It is observed that human intelligence is not one or

two directional only but it also incorporates other equally important and valuable

aspects of intelligence which are required to be recognized and developed. It leads to

the Theory of Multiple Intelligence (MI) and narration of different types of

intelligence supported by the Theory of MI. The chapter also presents a variety of

implementations of the Theory of MI. The Theory of MI has been utilized for students

in order to classify an appropriate career field according to the type of intelligence

he/she possesses.

In summary, it is observed from the extensive literature review on GA, FL and the

Theory of MI that hybridization of GA and FL has not yet been implemented in

education field and especially utilizing the Theory of MI. This observation becomes a

motivation towards designing of research application using hybridization of GA along

with FL. There are several hybrid models exists based on GA and FL integration. The

fundamentals of Genetic-Fuzzy (GF) hybridization as well as work done in the area of

GF are discussed in detail in next chapter.

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