A multimethod approach to multimodal function optimization

Multimodal functions play a central role in artificial intelligence. In this paper we attempt to address limitations in existing research on multimodal function optimization by developing a novel multi-method memetic algorithm (MMA). We empirically test MMA on synthetic and natural combinatorial optimization problems, including feature selection. Our initial experiments suggest that MMA preserves diversity well and consistently finds good solutions.