A genetic algorithm with self-adaptive niche sizing
Optimization of multimodal functions is hard for traditional optimization techniques. Holland's genetic algorithm combined with the concept of niche and speciation already showed success in such difficult problems. Some implementations of this concept exist, but the effective ones all lack flex...
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| Format: | Others |
| Language: | en |
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McGill University
1995
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| Online Access: | http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22847 |
| Summary: | Optimization of multimodal functions is hard for traditional optimization techniques. Holland's genetic algorithm combined with the concept of niche and speciation already showed success in such difficult problems. Some implementations of this concept exist, but the effective ones all lack flexibility by requiring either previous knowledge about the function to optimize or by imposing some fixed external schedule of exploration. We present an implementation of this concept using minimum spanning trees that is compared with a previous algorithm on random choices of input parameters. Our proposal does not require any a priori knowledge about the function. Two approaches using the minimum spanning trees are studied, one of which--the biggest proportion method (BPM)--outperforms its competitor on the battery of test problems and shows its capability of automatically adapting to the function to optimize. |
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