An LaF-CMAES hybrid for optimization in multi-modal search spaces
| Autor: | Antonio Bolufé-Röhler, Stephen Chen, Dania Tamayo-Vera |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
business.industry 02 engineering and technology Machine learning computer.software_genre Space exploration Task (project management) Set (abstract data type) Local optimum Modal 020204 information systems Simulated annealing 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | CEC |
| DOI: | 10.1109/cec.2017.7969386 |
| Popis: | Optimization in multi-modal search spaces requires both exploration and exploitation. The role of exploration is to find promising attraction basins, and the role of exploitation is to find the best solutions (i.e. the local optima) within these attraction basins. In many search techniques, the balance between exploration and exploitation can be adjusted by various parameter settings. An alternative approach is to develop (hybrid) techniques with distinct mechanisms for the task of exploration and the task of exploitation. We believe this second approach can be simpler and more effective. The presented LaF-CMAES hybrid involves relatively few design decisions (e.g. parameter selections), and it delivers highly competitive performance across a benchmark set of multi-modal functions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|