Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An Empirical Study of Multipopulation Genetic Programming
Genetic Programming and Evolvable Machines
Experimental Investigation of Three Distributed Genetic Programming Models
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Comparing Synchronous and Asynchronous Parallel and Distributed Genetic Programming Models
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
VRing: A Case for Building Application-Layer Multicast Rings (Rather Than Trees)
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
A P2P genetic algorithm environment for the internet
Communications of the ACM - Transforming China
Genetic programming in wireless sensor networks
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
A scalable cellular implementation of parallel genetic programming
IEEE Transactions on Evolutionary Computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
EvAg: a scalable peer-to-peer evolutionary algorithm
Genetic Programming and Evolvable Machines
A peer-to-peer approach to genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Android genetic programming framework
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
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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.