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GA2

An individual is a single solution. A chromosome is subdivided into genes. A gene is the GA’s representation of a single factor for a control factor. Each factor in the solution set corresponds to gene

in the chromosome. Chromosomes are encoded by bit strings are given below in Fig.1 Genes are the basic “instructions” for building a Generic Algorithms. A chromosome is a sequence of genes. Genes may describe a possible solution to a problem.

Fitness

The fitness of an individual in a genetic algorithm is the value of an objective function . For calculating fitness, the chromosome has to be first decoded and the objective function has to be evaluated. The fitness not only indicates how good the solution is, but also corresponds to how close the chromosome is to the optimal one.

5.1 Populations

A population is a collection of individuals. A population consists of a number of individuals being tested.

 The two important aspects of population used in

Genetic Algorithms are:

1. The initial population generation.

2. The population size.

5.2.Encoding

Encoding is a process of representing individual genes. The process can be performed using bits, numbers, trees, arrays, lists or any other objects. The encoding depends mainly on solving the problem. For example, one can encode directly real or integer numbers.

5.2.1 Binary Encoding

The most common way of encoding is a binary string, which would be represented as in Fig. 3 Each chromosome encodes a binary (bit) string. Each bit in the string can represent some characteristics of the solution. Every bit string therefore is a solution but not necessarily the best solution. Another possibility is that the whole string.

5..2.2 Octal Encoding

This encoding uses string made up of octal numbers (0–7).

5.2.3 Hexadecimal Encoding

This encoding uses string made up of hexadecimal numbers (0–9, A–F).

5.2.4 Permutation Encoding (Real Number Coding)

permutation encoding, every chromosome is a string of integer/real values, which represents number in a sequence.

5.2.5 Value Encoding

Every chromosome is a string of values and the values can be anything connected to the problem. This encoding produces best results for some special problems. On the other hand, it is often necessary to develop new genetic operator’s specific to the

problem. Direct value encoding can be used in problems, where some complicated values, such as real numbers, are used. Use of binary encoding for this type of problems would be very difficult.

In value encoding, every chromosome is a string of some values. Values can be anything connected to problem, form numbers, real numbers or chars to some complicated objects.

Value encoding is very good for some special problems. On the other hand, for this encoding is often necessary to develop some new crossover and mutation specific for the problem.