Name
Abstract | ||
|---|---|---|
This paper proposes a hybrid evolutionary algorithm (EA) dealing with population-based incremental learning (PBIL) and some efficient local search strategies. A simple PBIL using real codes is developed. The evolutionary direction and approximate gradient operators are integrated to the main procedure of PBIL. The method is proposed for single objective global optimization. The search performance of the developed hybrid algorithm for box-constrained optimization is compared with a number of well-established and newly developed evolutionary algorithms and meta-heuristics. It is found that, with the given optimization settings, the proposed hybrid optimizer outperforms the other EAs. The new derivative-free algorithm can maintain outstanding abilities of EAs. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25566-3_28 | LION |
Keywords | Field | DocType |
evolutionary direction,hybrid algorithm,new derivative-free algorithm,hybrid evolutionary algorithm,real code,single objective global optimization,evolutionary algorithm,box-constrained optimization,proposed hybrid optimizer,simple pbil,optimization setting,hybrid population-based incremental learning,evolutionary algorithms,meta heuristics | Population,Mathematical optimization,Hybrid algorithm,Global optimization,Evolutionary algorithm,Computer science,Operator (computer programming),Artificial intelligence,Local search (optimization),Population-based incremental learning,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
6 | 0.53 | 7 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sujin Bureerat | 1 | 64 | 11.36 |