A comparative review of approaches to prevent premature convergence in GA

Pandey, Hari Mohan, Chaudhary, Ankit and Mehrotra, Deepti (2014) A comparative review of approaches to prevent premature convergence in GA. Applied Soft Computing, 24 . pp. 1047-1077. ISSN 1568-4946 (doi:10.1016/j.asoc.2014.08.025)

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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.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 23479
Depositing User: Dr Hari Mohan Pandey
Date Deposited: 02 Feb 2018 11:21
Last Modified: 12 Sep 2018 15:29
URI: https://eprints.mdx.ac.uk/id/eprint/23479

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