title: 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.
authors.affiliation: Sandia National Laboratories, P.O. Box 5800, MS 1110, Albuquerque, NM 87185-1110, USA. wehart@sandia.gov
authors.forename: William E
authors.initials: WE
authors.lastname: Hart
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D011995:Recombination, Genetic