Hybrid evolutionary algorithms to solve scheduling problems
The choice of a search algorithm can play a vital role in the success of a scheduling application. Evolutionary algorithms (EAs) can be used to solve this kind of combinatorial optimization problems. Compared to conventional heuristics (CH) and local search techniques (LS), EAs are not well suited f...
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| Autores principales: | , , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2002
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/22062 |
| Aporte de: |
| Sumario: | The choice of a search algorithm can play a vital role in the success of a scheduling application. Evolutionary algorithms (EAs) can be used to solve this kind of combinatorial optimization problems. Compared to conventional heuristics (CH) and local search techniques (LS), EAs are not well suited for fine-tuninf those structures, which are very close to optimal solutions. Therefore, in complex problems, it is essential to build hybrid evolutionary algorithms (HEA) by incorporating CH and/or LS to provide fine-tuning.
EAs are good at global search but slow to converge, while local search is good for fine-tuning but often falls into local optima. The hybrid approach complements the properties of evolutionary algorithm and other techniques.
This research guide attempts to develop EAs hybridized with local search and conventional heuristics. They are incorporated at different stages of the evolutionary process. Either when the initial population is created, or in intermediate stages, or in the final population, or within the evolutionary process itself. |
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