An empirical evaluation of linkage learning strategies for multimodal optimization

Diversity preservation has shown to be very important for allowing the identification of the problem structure as much as for keeping several global optima during the process of evolutionary computation. The most important evolutionary algorithms currently available in the literature adopt diversity preservation techniques as supporting tools in the process, while they trust on more sophisticated models for the identification of the problem structure. This work evaluates a novel approach where a clustering algorithm plays a central role in the evolutionary process beyond maintaining the diversity. Empirical evaluation and comparison show the effectiveness of this new approach when solving multimodal optimization problems.