title: A filter-based evolutionary algorithm for constrained optimization.
abstract: We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.
authors.affiliation: Sandia National Laboratories, Discrete Algorithms and Mathematics Dept, P.O. Box 5800, MS 1110, Albuquerque, New Mexico 87185-1110, USA. Lmcleve@aol.com
authors.forename: Lauren
authors.initials: L
authors.lastname: Clevenger
authors.forename: Lauren
authors.initials: L
authors.lastname: Ferguson
authors.forename: William E
authors.initials: WE
authors.lastname: Hart
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D008962:Models, Theoretical; D009154:Mutation