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Crossover Operators in Genetic Algorithms: A Review

by Padmavathi Kora, Priyanka Yadlapalli
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 10
Year of Publication: 2017
Authors: Padmavathi Kora, Priyanka Yadlapalli
10.5120/ijca2017913370

Padmavathi Kora, Priyanka Yadlapalli . Crossover Operators in Genetic Algorithms: A Review. International Journal of Computer Applications. 162, 10 ( Mar 2017), 34-36. DOI=10.5120/ijca2017913370

@article{ 10.5120/ijca2017913370,
author = { Padmavathi Kora, Priyanka Yadlapalli },
title = { Crossover Operators in Genetic Algorithms: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 10 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 34-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number10/27282-2017913370/ },
doi = { 10.5120/ijca2017913370 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:41.527557+05:30
%A Padmavathi Kora
%A Priyanka Yadlapalli
%T Crossover Operators in Genetic Algorithms: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 10
%P 34-36
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Mutation crossover Selection travelling salesman problem

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