Improving cooperative GP ensemble with clustering and pruning for pattern classification
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic parallel programming: design and implementation
Evolutionary Computation
Expert Systems with Applications: An International Journal
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary dynamics on scale-free interaction networks
IEEE Transactions on Evolutionary Computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
An ensemble-based evolutionary framework for coping with distributed intrusion detection
Genetic Programming and Evolvable Machines
Differential evolution algorithms with cellular populations
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Effect of topology on diversity of spatially-structured evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
GP ensemble for distributed intrusion detection systems
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
P-CAGE: an environment for evolutionary computation in peer-to-peer systems
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Solving very large instances of the scheduling of independent tasks problem on the GPU
Journal of Parallel and Distributed Computing
A framework for modeling automatic offloading of mobile applications using genetic programming
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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A new parallel implementation of genetic programming (GP) based on the cellular model is presented and compared with both canonical GP and the island model approach. The method adopts a load-balancing policy that avoids the unequal utilization of the processors. Experimental results on benchmark problems of different complexity show the superiority of the cellular approach with respect to the canonical sequential implementation and the island model. A theoretical performance analysis reveals the high scalability of the implementation realized and allows to predict the size of the population when the number of processors and their efficiency are fixed.