When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Electronic versions
We show that multistart next ascent hillclimbing compares favourably to crowding-based genetic algorithms when solving instances of the multimodal problem generator. We conjecture that it is unlikely that any practical evolutionary algorithm is capable of solving this type of problem instances faster than the multistart hillclimbing strategy.
Keywords
- niching, multimodal problem generator, multimodal optimization, hillclimbing, genetic algorithms
| Original language | English |
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| Title of host publication | Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery |
| Pages | 1421–1422 |
| ISBN (print) | 9781450334884 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
Publication series
| Name | GECCO Companion '15 |
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| Publisher | Association for Computing Machinery |