Publications Details
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 convergence guarantees on a broad class of nonconvex continuous problems. In previous work we have analyzed the empirical performance of EPSAs. This paper revisits that analysis and extends it to a more general model of mutation. We experimentally evaluate how the choice of the set of mutation offsets affects optimization performance for EPSAs. Additionally, 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.