Paper Info
Title
A Convergence Analysis of Unconstrained and Bound Constrained Evolutionary Pattern Search
Abstract
We present and analyze a class of evolutionary algorithms for unconstrained and bound constrained optimization on R(n): evolutionary pattern search algorithms (EPSAs). EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. We show that EPSAs can be cast as stochastic pattern search methods, and we use this observation to prove that EPSAs have a probabilistic, weak stationary point convergence theory. This convergence theory is distinguished by the fact that the analysis does not approximate the stochastic process of EPSAs, and hence it exactly characterizes their convergence properties.
Year
DOI
Venue
2001
10.1162/10636560151075095
Evolutionary Computation
Keywords
DocType
Volume
evolutionary pattern search,bound constraints,local convergence,parameter adaptation,objective function,pattern search,evolutionary algorithm,design optimization
Journal
9
Issue
ISSN
Citations 
1
1063-6560
15
PageRank 
References 
Authors
1.26
18
1
Name
Order
Citations
PageRank
William E. Hart11028141.71