Effects of diversity control in single-objective and multi-objective genetic algorithms |
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Authors: | Nachol Chaiyaratana Theera Piroonratana Nuntapon Sangkawelert |
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Institution: | (1) Research and Development Center for Intelligent Systems, King Mongkut’s Institute of Technology North Bangkok, 1518 Piboolsongkram Road, Bangsue, Bangkok, 10800, Thailand;(2) Department of Production Engineering, King Mongkut’s Institute of Technology North Bangkok, 1518 Piboolsongkram Road, Bangsue, Bangkok, 10800, Thailand |
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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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Keywords: | Benchmarking Diversity control Multi-objective genetic algorithm Single-objective genetic algorithm |
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