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.


A preliminary version of this article appeared in Friedrich et al.(2008).

Tobias Friedrich and Dirk Sudholt were partially supported by postdoctoral fellowships from the German Academic Exchange Service. Pietro S. Oliveto was supported by an EPSRC grant (EP/C520696/1). Dirk Sudholt and Carsten Witt were partially supported by the Deutsche Forschungsgemeinschaft (DFG) as a part of the Collaborative Research Center “Computational Intelligence” (SFB 531).

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