Niching in an ES/EP context

Niching or speciation is a particularly appropriate for multimodal function optimization. An irony in EC research is that genetic algorithms (GAs) are not touted primarily as function optimizers yet all reported niching research is in the GA context. Evolution strategies (ES) and major variants of evolutionary programming are better suited by design for global optimization. Borrowing methods that have been reported for niching in GAs, we have applied them to an EC algorithm that resembles ES in structure. We have found that the selection methods in ES, e.g., (μ+λ), interact satisfactorily with niching strategies used in GAs. On the other hand, adapting selection methods such as SUS that minimize bias does not lead to favorable results. This is counter to expectations but can be reconciled with prevailing theories. We conclude with a conjecture concerning a lower bound on population size for multimodal optimization