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Self-Organizing Genetic Algorithm: A Survey

by Amouda Nizam, Buvaneswari Shanmugham
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 18
Year of Publication: 2013
Authors: Amouda Nizam, Buvaneswari Shanmugham
10.5120/11025-5659

Amouda Nizam, Buvaneswari Shanmugham . Self-Organizing Genetic Algorithm: A Survey. International Journal of Computer Applications. 65, 18 ( March 2013), 25-32. DOI=10.5120/11025-5659

@article{ 10.5120/11025-5659,
author = { Amouda Nizam, Buvaneswari Shanmugham },
title = { Self-Organizing Genetic Algorithm: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 18 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number18/11025-5659/ },
doi = { 10.5120/11025-5659 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:19:12.284027+05:30
%A Amouda Nizam
%A Buvaneswari Shanmugham
%T Self-Organizing Genetic Algorithm: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 18
%P 25-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Self-organization systems are an increasingly attractive dynamic processes without a central control, emerge global order from local interactions in a bottom up approach. The advantage of blending the concept of self-organization enhances the working efficiency of other techniques to find a solution of huge search problem. Genetic Algorithms (GA) is such a technique, inspired by the natural evolution process, used to solve difficult optimization problem of large space solution, for an example, multiple sequence alignment (MSA) problem in a bioinformatics research. Self-organization technique automates the selection of appropriate parameter values of GA during execution without the user's intervention. An attempt towards applying Self-organizing Genetic Algorithm (SOGA) on MSA requires a complete knowledge of the various parameters of SO and its relationships. This lead us to make a complete survey on inherent properties of SO and the method of blending GA in order to develop a self-organizing genetic algorithm (SOGA) for MSA. The aim of the research is to make use of the efficiency of GA without getting any input from the non-trained users to tune the parameters in order to achieve the expected result.

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Index Terms

Computer Science
Information Sciences

Keywords

Crossover Mutation Selection Self-organizing genetic algorithm

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