Crossover and Mutation Operations in GA-Genetic Algorithm

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.