Population Clustering in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{eurogp06:XieZhangAndreae,

• author = "Huayang Xie and Mengjie Zhang and Peter Andreae",
• title = "Population Clustering in Genetic Programming",
• editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art",
• booktitle = "Proceedings of the 9th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3905",
• year = "2006",
• address = "Budapest, Hungary",
• month = "10 - 12 " # apr,
• organisation = "EvoNet",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-33143-3",
• pages = "190--201",
• DOI = "doi:10.1007/11729976_17",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• abstract = "This paper proposes an approach to reducing the cost of fitness evaluation whilst improving the effectiveness in Genetic Programming (GP). In our approach, the whole population is first clustered by a heuristic called fitness-case-equivalence. Then a cluster representative is selected for each cluster. The fitness value of the representative is calculated on all training cases. The fitness is then directly assigned to other members in the same cluster. Subsequently, a clustering tournament selection method replaces the standard tournament selection method. A series of experiments were conducted to solve a symbolic regression problem, a binary classification problem, and a multi-class classification problem. The experiment results show that the new GP system significantly outperforms the standard GP system on these problems.",
• notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006",

}

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang Peter Andreae

Citations