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Effects of diversity control in single-objective and multi-objective genetic algorithms

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Abstract

This paper covers an investigation on the effects of diversity control in the search performances of single-objective and multi-objective genetic algorithms. The diversity control is achieved by means of eliminating duplicated individuals in the population and dictating the survival of non-elite individuals via either a deterministic or a stochastic selection scheme. In the case of single-objective genetic algorithm, onemax and royal road R 1 functions are used during benchmarking. In contrast, various multi-objective benchmark problems with specific characteristics are utilised in the case of multi-objective genetic algorithm. The results indicate that the use of diversity control with a correct parameter setting helps to prevent premature convergence in single-objective optimisation. Furthermore, the use of diversity control also promotes the emergence of multi-objective solutions that are close to the true Pareto optimal solutions while maintaining a uniform solution distribution along the Pareto front.

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Authors and Affiliations

  1. Research and Development Center for Intelligent Systems, King Mongkut’s Institute of Technology North Bangkok, 1518 Piboolsongkram Road, Bangsue, Bangkok, 10800, Thailand

    Nachol Chaiyaratana

  2. Department of Production Engineering, King Mongkut’s Institute of Technology North Bangkok, 1518 Piboolsongkram Road, Bangsue, Bangkok, 10800, Thailand

    Theera Piroonratana & Nuntapon Sangkawelert

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  1. Nachol Chaiyaratana
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  2. Theera Piroonratana
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  3. Nuntapon Sangkawelert
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Correspondence to Nachol Chaiyaratana.

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Chaiyaratana, N., Piroonratana, T. & Sangkawelert, N. Effects of diversity control in single-objective and multi-objective genetic algorithms. J Heuristics 13, 1–34 (2007). https://doi.org/10.1007/s10732-006-9003-1

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