Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Heterogeneity through Proactivity: Enhancing Distributed EAs
3PGCIC '12 Proceedings of the 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
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In this abstract we describe a proactive strategy followed by a distributed evolutionary algorithm to adapt its migration policy. The proactive decision is made locally within each subpopulation, ant it is based on the entropy of that subpopulation. In that way, each subpopulation can ask for more/less frequent migrations from its neighbors in order to maintain the genetic diversity at a desired level, thus avoiding the subpopulations to get trapped into local minima. We conduct computational experiments on a set of different problems and it is shown that our proactive approach outperforms classical dEA settings by reaching accurate solutions in a lower number of generations.