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Genetic Algorithm: A Search of Complex Spaces

by Namita Khurana, Anju Rathi, Akshatha.P.S
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 25 - Number 7
Year of Publication: 2011
Authors: Namita Khurana, Anju Rathi, Akshatha.P.S
10.5120/3045-4136

Namita Khurana, Anju Rathi, Akshatha.P.S . Genetic Algorithm: A Search of Complex Spaces. International Journal of Computer Applications. 25, 7 ( July 2011), 13-17. DOI=10.5120/3045-4136

@article{ 10.5120/3045-4136,
author = { Namita Khurana, Anju Rathi, Akshatha.P.S },
title = { Genetic Algorithm: A Search of Complex Spaces },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 25 },
number = { 7 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume25/number7/3045-4136/ },
doi = { 10.5120/3045-4136 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:11:07.369352+05:30
%A Namita Khurana
%A Anju Rathi
%A Akshatha.P.S
%T Genetic Algorithm: A Search of Complex Spaces
%J International Journal of Computer Applications
%@ 0975-8887
%V 25
%N 7
%P 13-17
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Living species solve very complex problem of optimization through the mechanism of evolution and natural selection. Genetic Algorithm has been a field of active interest and applied to solve problems almost in all the fields like Computer Science, Electrical Engg., Mechanical Engg., Optimization, Biology and Image Processing etc. One important application of Genetic Algorithm is to search complex spaces and function optimization. A genetic algorithm begins its search with random solution of the problem. The initial population is evolved to new population using Genetic operators like reproduction, crossover and mutation. A Genetic Algorithm keeps evolving the successive populations unless some criterion is met or a reasonable acceptable solution is found. In this paper Genetic Algorithm has been applied to schwefel function to find the best fit chromosome so far.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Schwefel function Crossover Mutation Selection

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