A comparative review of approaches to prevent premature convergence in GA

Article


Pandey, H., Chaudhary, A. and Mehrotra, D. 2014. A comparative review of approaches to prevent premature convergence in GA. Applied Soft Computing. 24, pp. 1047-1077. https://doi.org/10.1016/j.asoc.2014.08.025
TypeArticle
TitleA comparative review of approaches to prevent premature convergence in GA
AuthorsPandey, H., Chaudhary, A. and Mehrotra, D.
Abstract

This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs).Genetic Algorithm belongs to the set of nature inspired algorithms. The applications of GA cover wide domains such as optimization, pattern recognition, learning, scheduling, economics, bioinformatics, etc.Fitness function is the measure of GA, distributed randomly in the population. Typically, the particular value for each gene start dominating as the search evolves. During the evolutionary search, fitness
decreases as the population converges, this leads to the problems of the premature convergence and slow finishing. In this paper, a detailed and comprehensive survey of different approaches implemented to prevent premature convergence with their strengths and weaknesses is presented. This paper also discusses the details about GA, factors affecting the performance during the search for global optima and brief details about the theoretical framework of Genetic algorithm. The surveyed research is organizedin a systematic order. A detailed summary and analysis of reviewed literature are given for the quick review. A comparison of reviewed literature has been made based on different parameters. The underlying
motivation for this paper is to identify methods that allow the development of new strategies to prevent premature convergence and the effective utilization of genetic algorithms in the different area of research.

Research GroupArtificial Intelligence group
PublisherElsevier
JournalApplied Soft Computing
ISSN1568-4946
Publication dates
Online06 Sep 2014
Print01 Nov 2014
Publication process dates
Deposited02 Feb 2018
Accepted11 Aug 2014
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1016/j.asoc.2014.08.025
LanguageEnglish
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