Multimodal Optimization
Mike Preuß · 2015
Multimodal optimization is currently getting established as a research direction that collects approaches from various domains of evolutionary computation that strive for delivering multiple very good solutions at once. We start with discussing why this is actually useful and therefore provide some real-world examples. From that on, we set up several scenarios and list currently employed and potentially available performance measures. This part also calls for user interaction: currently, it is very open what the actual targets of multimodal optimization shall be and how the algorithms shall be compared experimentally. In-tutorial discussion of this topic will be encouraged.