A performance analysis of evolutionary pattern search with generalized mutation steps

Evolutionary pattern search algorithms (EPSAs) are a class of evolutionary algorithms (EAs) that have stationary-point convergence guarantees on a broad class of nonconvex continuous problems. We have analyzed the empirical performance of EPSAs. This paper revisits that analysis and extends it to a more general model of mutation. We evaluate experimentally how the choice of the set of mutation offsets affects optimization performance for EPSAs. In addition, we compare EPSAs to self-adaptive EAs with respect to robustness and rate of optimization. All experiments employ a suite of test functions representing a range of modality and number of multiple minima