Name
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1162/106365603321828998 | Evolutionary Computation |
Keywords | Field | DocType |
evolution strategy,pattern search,evolutionary algorithm,local search | Memetic algorithm,Convergence (routing),Population,Evolutionary algorithm,Artificial intelligence,Symbolic convergence theory,Pattern search,Mathematical optimization,Algorithm,Stationary point,Local search (optimization),Machine learning,Mathematics | Journal |
Volume | Issue | Citations |
11 | 1 | 7 |
PageRank | References | Authors |
0.78 | 20 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| William E. Hart | 1 | 1028 | 141.71 |