期刊
EVOLUTIONARY COMPUTATION
卷 17, 期 4, 页码 455-476出版社
MIT PRESS
DOI: 10.1162/evco.2009.17.4.17401
关键词
Diversity; runtime analysis; fitness sharing; deterministic crowding; exploration
资金
- German Academic Exchange Service
- EPSRC [EP/C520696/1]
- Deutsche Forschungsgemeinschaft (DFG) [SFB 531]
Maintaining diversity is important for the performance of evolutionary algorithms. Diversity-preserving mechanisms can enhance global exploration of the search space and enable crossover to find dissimilar individuals for recombination. We focus on the global exploration capabilities of mutation-based algorithms. Using a simple bimodal test function and rigorous runtime analyses, we compare well-known diversity-preserving mechanisms like deterministic crowding, fitness sharing, and others with a plain algorithm without diversification. We show that diversification is necessary for global exploration, but not all mechanisms succeed in finding both optima efficiently. Our theoretical results are accompanied by additional experiments for different population sizes.
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