Asynchronous collaborative search using adaptive co-evolving subpopulations
Pages 2575 - 2582
Abstract
Efficient recombination and selection strategies in evolutionary search models have a great impact on the quality of detected solutions. Evolving populations of adaptive individuals can potentiallly trigger important results for the design of evolutionary models. The Geometric Collaborative Evolutionary (GCE) model takes this approach by integrating agent-based features (such as autonomy and communication) into the evolving population. Each individual is able to act like an agent in the sense that communication with other individuals is possible and facilitates the selection of a mate for recombination. The contribution of this paper is twofold: (i) the benefits of GCE having an agent-inspired component are assessed in a set of numerical experiments for the optimization of difficult real-valued functions, and (ii) the GCE algorithm is applied with successful results for solving the density classification problem in one dimensional binary state Cellular Automata (CA). The GCE model clearly benefits from its agent-inspired component obtaining better numerical results compared to its GCE variant with no agent-inspired behavior. The organization of population in dynamic societies with different strategies for recombination plays an important role in the search process. Furthermore, numerical results and comparisons emphasize a better performance of the GCE model in evolving CA rules compared to other evolutionary models.
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Index Terms
- Asynchronous collaborative search using adaptive co-evolving subpopulations
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Published: 08 July 2009
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