Locally-Adaptive and Memetic Evolutionary Pattern Search Algorithms

Abstract

Recent convergence analyses of evolutionary pattern search algorithms (EPSAs) have shown that these methods have a weak stationary point convergence theory for a broad class of unconstrained and linearly constrained problems. This paper describes how the convergence theory for EPSAs can be adapted to allow each individual in a population to have its own mutation step length (similar to the design of evolutionary programing and evolution strategies algorithms). These are called locally-adaptive EPSAs (LA-EPSAs) since each individual's mutation step length is independently adapted in different local neighborhoods. The paper also describes a variety of standard formulations of evolutionary algorithms that can be used for LA-EPSAs. Further, it is shown how this convergence theory can be applied to memetic EPSAs, which use local search to refine points within each iteration.

DOI: 10.1162/evco.2003.11.1.29

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Cite this paper

@article{Hart2003LocallyAdaptiveAM, title={Locally-Adaptive and Memetic Evolutionary Pattern Search Algorithms}, author={William E. Hart}, journal={Evolutionary computation}, year={2003}, volume={11 1}, pages={29-51} }