Performance Analysis of Different Selection Techniques in Genetic Algorithm

This Paper compares the performance of different selection techniques in GA using De Jongs function1 as function to be used fitness function. Genetic algorithm is one of the optimization techniques that can be used to solve the problems of function maximization. It can be said as a search procedure inspired by principles from natural selection and genetics [LOB00]. It is often used as an optimization method to solve problems where very little is known about the objective function. The operation of the genetic algorithm is very simple. It starts with a population of random individuals, each corresponding to a particular candidate solution to the problem to be solved. Then, the best individuals survive, mate, and create offspring, originating a new population of individuals. This process is repeated a number of times, and usually leads to better and better individuals.