Name
Title | ||
|---|---|---|
Parallel Hypervolume-Guided Hyperheuristic for Adapting the Multi-objective Evolutionary Island Model |
Abstract | ||
|---|---|---|
This work presents a new parallel model for the solution of multi-objective optimization problems. The model is based on the cooperation of a set of evolutionary algorithms. The main aim is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. The proposed model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The hyperheuristic is guided by the measurement of the hypervolume achieved by different optimization methods. The model grants more computational resources to those schemes that show a more promising behaviour. The computational results obtained for some tests available in the literature demonstrate the validity of the proposed model. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-03211-0_22 | Studies in Computational Intelligence |
Keywords | Field | DocType |
evolutionary algorithm,hybrid algorithm | Mathematical optimization,Hybrid algorithm,Evolutionary algorithm,Computer science,Multi-objective optimization,Island model,Evolutionary programming,Optimization problem,Generality | Conference |
Volume | ISSN | Citations |
236 | 1860-949X | 1 |
PageRank | References | Authors |
0.35 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Coromoto León | 1 | 231 | 25.71 |
| Gara Miranda | 2 | 188 | 18.16 |
| Eduardo Segredo | 3 | 77 | 11.02 |
| Carlos Segura | 4 | 216 | 21.44 |