Solving complex real-world problems using evolutionary computation is a CPU time-consuming task that requires a large amount of computational resources. Peer-to-Peer (P2P) computing has recently revealed as a powerful way to harness these resources and efficiently deal with such problems. In this paper, we present a P2P implementation of Genetic Programming based on the JXTA technology. To run genetic programs we use a distributed environment based on a hybrid multi-island model that combines the island model with the cellular model. Each island adopts a cellular genetic programming model and the migration occurs among neighboring peers. The implementation is based on a virtual ring topology. Three different termination criteria (effort, time and max-gen) have been implemented. Experiments on some popular benchmarks show that the approach presents a accuracy at least comparable with classical distributed models, retaining the obvious advantages in terms of decentralization, fault tolerance and scalability of P2P systems. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Folino, G., & Spezzano, G. (2006). P-CAGE: An environment for evolutionary computation in peer-to-peer systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3905 LNCS, pp. 341–350). https://doi.org/10.1007/11729976_31
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