CASOS Working PAPER

"A Performance Analysis of Evolutionary Pattern Search with Generalized Mutation Steps" (PDF: 91KB)
Authors: William E. Hart, Keith O. Hunter

Abstract
Abstract- 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 selfadaptive 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.