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Article:New Approach to Standard Genetic Algorithm

by Fariborz Ahmadi, Amir Sheikhahmadi, Reza Tati, Soraia Ahmadi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 10
Year of Publication: 2011
Authors: Fariborz Ahmadi, Amir Sheikhahmadi, Reza Tati, Soraia Ahmadi
10.5120/3949-5503

Fariborz Ahmadi, Amir Sheikhahmadi, Reza Tati, Soraia Ahmadi . Article:New Approach to Standard Genetic Algorithm. International Journal of Computer Applications. 32, 10 ( October 2011), 46-50. DOI=10.5120/3949-5503

@article{ 10.5120/3949-5503,
author = { Fariborz Ahmadi, Amir Sheikhahmadi, Reza Tati, Soraia Ahmadi },
title = { Article:New Approach to Standard Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 10 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 46-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number10/3949-5503/ },
doi = { 10.5120/3949-5503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:52.819261+05:30
%A Fariborz Ahmadi
%A Amir Sheikhahmadi
%A Reza Tati
%A Soraia Ahmadi
%T Article:New Approach to Standard Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 10
%P 46-50
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Genetic algorithm (GA) uses the principle of natural selection of Darwin to find the most suitable formula for prediction or pattern matching. Shortly, it is said that GA is a programming technique that uses the genetic evolution to solve a problem. The problem that should be solved is the input of evolution and its solution are encoded according to the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in previous generations. To from this disadvantage, All individuals in this work divided into two categories, namely, male (chromosome) and female (ovum). In crossover operation, only one chromosome and one ovum can be existed and under some conditions these two individuals recombine with each other. In this work, a new approach has been invented and the results of implementation and evaluations show the technique efficiency in proportion to standard genetic algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

chromosome ovum ancestor