A multiple mitosis genetic algorithm

K. Kamil, K. H. Chong, H. Hashim, S. A. Shaaya

Research output: Contribution to journalArticle

Abstract

Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum. This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.

Original languageEnglish
Pages (from-to)252-258
Number of pages7
JournalIAES International Journal of Artificial Intelligence
Volume8
Issue number3
DOIs
Publication statusPublished - 01 Jan 2019

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Genetic algorithms
Genetic algorithm
Crossover
Optimization problem

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems and Management
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

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A multiple mitosis genetic algorithm. / Kamil, K.; Chong, K. H.; Hashim, H.; Shaaya, S. A.

In: IAES International Journal of Artificial Intelligence, Vol. 8, No. 3, 01.01.2019, p. 252-258.

Research output: Contribution to journalArticle

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