DocumentCode :
618182
Title :
Island model genetic programming based on frequent trees
Author :
Ono, Keishi ; Hanada, Yoshiko ; Kumano, Masahito ; Kimura, Mizue
Author_Institution :
Dept. of Electron. & Inf., Ryukoku Univ., Kyoto, Japan
fYear :
2013
fDate :
20-23 June 2013
Firstpage :
2988
Lastpage :
2995
Abstract :
The Island Model encourages genetic diversity, and often displays better search performance than single population models. In order to enhance the Island Model in the framework of genetic programming (GP), we propose a novel migration strategy based on frequent trees, where the frequent trees in an island mean the sub-trees appearing frequently among the individuals in the island. The proposed method evaluates each island by measuring its activation level in terms of not only how high the best fitness value is but also how many types of frequent trees are newly created, and then makes several individuals migrate from an island with high activation level to an island with low activation level, and vice versa. Using three benchmark problems widely adopted in the literature, we demonstrate that performance improvement can be achieved through incorporating the information of frequent trees into a migration strategy, and the proposed method significantly outperforms a typical method of the Island Model GP.
Keywords :
genetic algorithms; trees (mathematics); GP; fitness value; frequent tree; genetic diversity; genetic programming; island model; migration strategy; Benchmark testing; Computational modeling; Genetic programming; Sociology; Statistics; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location :
Cancun
Print_ISBN :
978-1-4799-0453-2
Electronic_ISBN :
978-1-4799-0452-5
Type :
conf
DOI :
10.1109/CEC.2013.6557933
Filename :
6557933
Link To Document :
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