Cluster analysis of genetic algorithm results
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| Natura: | Artículo científico |
| Lingua: | en |
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Asociación Española para la Inteligencia Artificial
2005
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| author | Katarzyna Adamska |
| author_facet | Katarzyna Adamska |
| contents | Cluster analysis of genetic algorithm results Katarzyna Adamska Ingeniería clustering genetic algorithms global optimization finite mixture model The work is concerned on the problem of approximation of central parts of basins of attraction of anobjective in continuous global optimization problems. It presents the general strategy of Clustered GeneticSearch (CGS), which consists in finding clusters in a genetic sample to get the approximations of basins ofattraction of an objective. DR-CGS is an instance of CGS which utilizes a construction of a Finite MixtureModel of normal componenets as a clustering method. CR-CGS brings wide opportunities of asymptoticanalysis. Due to features of a normal mixture, it also allows for very easy definition of approximations ofbasins of attraction Presented computational tests illustrate how the method works and are a practicalevidence of its good results 2005 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92592803 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.28 Vol.9 |
| format | Artículo científico |
| id | redalyc_92592803 |
| institution | Redalyc |
| language | en |
| publishDate | 2005 |
| publisher | Asociación Española para la Inteligencia Artificial |
| spellingShingle | Cluster analysis of genetic algorithm results Katarzyna Adamska Ingeniería clustering genetic algorithms global optimization finite mixture model Cluster analysis of genetic algorithm results Katarzyna Adamska Ingeniería clustering genetic algorithms global optimization finite mixture model The work is concerned on the problem of approximation of central parts of basins of attraction of anobjective in continuous global optimization problems. It presents the general strategy of Clustered GeneticSearch (CGS), which consists in finding clusters in a genetic sample to get the approximations of basins ofattraction of an objective. DR-CGS is an instance of CGS which utilizes a construction of a Finite MixtureModel of normal componenets as a clustering method. CR-CGS brings wide opportunities of asymptoticanalysis. Due to features of a normal mixture, it also allows for very easy definition of approximations ofbasins of attraction Presented computational tests illustrate how the method works and are a practicalevidence of its good results 2005 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92592803 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.28 Vol.9 |
| title | Cluster analysis of genetic algorithm results |
| topic | Ingeniería clustering genetic algorithms global optimization finite mixture model |
| url | https://www.redalyc.org/articulo.oa?id=92592803 |