Article (Scientific journals)
Cellular genetic algorithms without additional parameters
Dorronsoro, Bernabe; Bouvry, Pascal
2013In Journal of Supercomputing, 63 (3), p. 816-835
Peer reviewed
 

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Keywords :
adaptive algorithms; cellular populations; evolutionary algorithms
Abstract :
[en] Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to close ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore, in a better performance of the algorithm. However, it supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. We propose in this work two innovative cGAs with new adaptive techniques that allow removing the neighborhood and population shape from the algorithm’s configuration. As a result, the new adaptive cGAs are highly competitive (statistically) with all the compared cGAs in terms of the average solutions found in the continuous and combinatorial domains, while finding, in general, the best solutions for the considered problems, and with less computational effort.
Disciplines :
Computer science
Author, co-author :
Dorronsoro, Bernabe
Bouvry, Pascal ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Language :
English
Title :
Cellular genetic algorithms without additional parameters
Publication date :
March 2013
Journal title :
Journal of Supercomputing
eISSN :
0920-8542
Publisher :
Springer
Special issue title :
Special section on Parallel Nature-Inspired Optimization
Volume :
63
Issue :
3
Pages :
816-835
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 19 November 2013

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