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| Content Provider | Springer Nature Link |
|---|---|
| Author | LaTorre, Antonio Muelas, Santiago Peña, José María |
| Copyright Year | 2010 |
| Abstract | Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue. |
| Starting Page | 2187 |
| Ending Page | 2199 |
| Page Count | 13 |
| File Format | |
| ISSN | 14327643 |
| Journal | Soft Computing |
| Volume Number | 15 |
| Issue Number | 11 |
| e-ISSN | 14337479 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2010-09-11 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Continuous optimization Multiple offspring sampling Scalability Control, Robotics, Mechatronics Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Computational Intelligence |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Software Geometry and Topology |
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