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Crossover Operators in Genetic Algorithms: A Review

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Padmavathi Kora, Priyanka Yadlapalli

Padmavathi Kora and Priyanka Yadlapalli. Crossover Operators in Genetic Algorithms: A Review. International Journal of Computer Applications 162(10):34-36, March 2017. BibTeX

	author = {Padmavathi Kora and Priyanka Yadlapalli},
	title = {Crossover Operators in Genetic Algorithms: A Review},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {10},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {34-36},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2017913370},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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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Genetic Algorithm; Mutation; crossover; Selection; travelling salesman problem