The Journal of Supercomputing

, Volume 63, Issue 3, pp 816–835 | Cite as

Cellular genetic algorithms without additional parameters



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.


Adaptive algorithms Cellular populations Evolutionary algorithm 



This work was completed with the support of Luxembourg FNR GreenIT project (C09/IS/05).


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Interdisciplinary Centre for Security, Reliability and TrustUniversity of LuxembourgLuxembourgLuxembourg
  2. 2.Faculty of Sciences, Technology, and CommunicationsUniversity of LuxembourgLuxembourgLuxembourg

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