Research Article

Self-Organizing Genetic Algorithm: A Survey

by  Amouda Nizam, Buvaneswari Shanmugham
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Issue 18
Published: March 2013
Authors: Amouda Nizam, Buvaneswari Shanmugham
10.5120/11025-5659
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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 },
                        year    = { 2013 },
                        volume  = { 65 },
                        number  = { 18 },
                        pages   = { 25-32 },
                        doi     = { 10.5120/11025-5659 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2013
                        %A Amouda Nizam
                        %A Buvaneswari Shanmugham
                        %T Self-Organizing Genetic Algorithm: A Survey%T 
                        %J International Journal of Computer Applications
                        %V 65
                        %N 18
                        %P 25-32
                        %R 10.5120/11025-5659
                        %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
No index terms available.
Keywords

Crossover Mutation Selection Self-organizing genetic algorithm

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