Abstract
Solving complex real-world problems using evolutionary computation is a CPU time-consuming task that requires a large amount of computational resources. Peerto-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 P-CAGE: a P2P environment for Genetic Programming based on the JXTA protocols. P-CAGE is based on a hybrid multi-island model that combines the island model with the cellular model. Each island adopts a cellular model and the migration occurs between neighboring peers placed in a virtual ring topology. Three different termination criteria (effort, time and maxgen) have been implemented. Experiments were conducted on some popular benchmarks and scalability, accuracy and the effect of migration have been studied. Performance are at least comparable with classical distributed models, retaining the obvious advantages in terms of decentralization, fault tolerance and scalability of P2P systems. We also demonstrated the important effect of migration in accelerating the convergence.
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