Name
Abstract | ||
|---|---|---|
A challenge in hybrid evolutionary algorithms is to define efficient strategies to cover all search space, applying local search only in actually promising search areas. This paper proposes a way of detecting promising search areas based on clustering. In this approach, an iterative clustering works simultaneously to an evolutionary algorithm accounting the activity (selections or updatings) in search areas and identifying which of them deserves a special interest. The search strategy becomes more aggressive in such detected areas by applying local search. A first application to unconstrained numerical optimization is developed, showing the competitiveness of the method. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28645-5_39 | ADVANCES IN ARTIFICIAL INTELLIGENCE - SBIA 2004 |
Keywords | Field | DocType |
hybrid evolutionary algorithms,unconstrained numerical optimization | Incremental heuristic search,Guided Local Search,Evolutionary algorithm,Computer science,Artificial intelligence,Local search (optimization),Cluster analysis,Genetic algorithm,Machine learning,Tabu search,Best-first search | Conference |
Volume | ISSN | Citations |
3171 | 0302-9743 | 23 |
PageRank | References | Authors |
1.31 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre César Muniz De Oliveira | 1 | 83 | 8.30 |
| Luiz Antonio Nogueira Lorena | 2 | 498 | 36.72 |