Effects of diversity control in single-objective and multi-objective genetic algorithms
| Autor: | Nachol Chaiyaratana, Nuntapon Sangkawelert, Theera Piroonratana |
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| Rok vydání: | 2006 |
| Předmět: |
Mathematical optimization
education.field_of_study Control and Optimization Computer Networks and Communications Computer science Quality control and genetic algorithms Population Management Science and Operations Research Genetic operator Multi-objective optimization Artificial Intelligence Genetic algorithm Genetic representation education Software Selection (genetic algorithm) Information Systems Premature convergence |
| Zdroj: | Journal of Heuristics. 13:1-34 |
| ISSN: | 1572-9397 1381-1231 |
| DOI: | 10.1007/s10732-006-9003-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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