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
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