Crossover and Mutation Operations in GA-Genetic Algorithm
S. Sangari Devi · 2013
Genetic Algorithms GA are search algorithms based on the principles of natural selection and genetics. GA evolves a population of initial individuals to a population of high quality individuals, where each individual represents a solution to the problem to be solved. Each individual is called chromosome and is composed of predetermined number of genes. The quality of each rule is measured by a fitness function as the quantitative representation of each rule's adaptation. The genetic algorithm can be viewed as two stage process. It starts with the current population. Selection is applied to the current population to create an intermediate population. Then recombination and mutation are applied to the intermediate population to create the next population. The process of going from the current population to the next population constitutes one generation in the execution of a genetic algorithm. Crossover is applied to randomly paired strings with a probability denoted Pc. A pair of strings is picked with probability Pc for recombination. These strings form two new strings that are inserted into the next population. After recombination, mutation operator is applied. In this paper mutation and crossover operations are discussed with GA-Genetic Algorithm. The search for an appropriate hypothesis begins with a population of initial hypotheses strings. Members of the current population give rise to the next generation by means of operations such as crossover and mutation. At each step, the hypotheses in the current population are evaluated by a fitness function. The fit hypotheses are selected probabilistically for producing the next generation. The basic representational structure of a genetic program is a directed, acyclic graph, also known as a tree, with symbols from the defined language associated with each position in the tree. Accordingly, terminals occupy the leaves of the trees while functions the internal positions. The Overall structure of a program tree is determined by the number of input parameters to the functions that occupy the tree's internal points. A function that takes three arguments, for instance, will have three associated sub trees below it, one per input argument. The number of parameters required for a function is the only syntactic constraint associated with genetic programming trees.