Experimental comparison of six population-based algorithms for continuous black box optimization
Language English Country United States Media print-electronic
Document type Comparative Study, Journal Article, Research Support, Non-U.S. Gov't
PubMed
22708972
DOI
10.1162/evco_a_00083
Knihovny.cz E-resources
- MeSH
- Algorithms * MeSH
- Benchmarking methods MeSH
- Humans MeSH
- Numerical Analysis, Computer-Assisted MeSH
- Models, Theoretical * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
Six population-based methods for real-valued black box optimization are thoroughly compared in this article. One of them, Nelder-Mead simplex search, is rather old, but still a popular technique of direct search. The remaining five (POEMS, G3PCX, Cauchy EDA, BIPOP-CMA-ES, and CMA-ES) are more recent and came from the evolutionary computation community. The recently proposed comparing continuous optimizers (COCO) methodology was adopted as the basis for the comparison. The results show that BIPOP-CMA-ES reaches the highest success rates and is often also quite fast. The results of the remaining algorithms are mixed, but Cauchy EDA and POEMS are usually slow.
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