Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Genetic algorithms are very effective way of finding a very effective way of quickly finding a reasonable solution to a complex problem. Performance of genetic algorithms mainly depends on type of genetic operators which involve crossover and mutation operators. 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.
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Kora et al. (2017) studied this question.