Study of Various Crossover Operators in Genetic Algorithms

Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Performance of genetic algorithms mainly depends on type of genetic operators – Selection, Crossover, Mutation and Replacement used in it. Different crossover and mutation operators exist to solve the problem that involves large population size. Example of such a problem is travelling sales man problem, which is having a large set of solution. In this paper we will discuss different crossover operators that help in solving the problem.