Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Brook for GPUs: stream computing on graphics hardware
ACM SIGGRAPH 2004 Papers
Moving Scientific Codes to Multicore Microprocessor CPUs
Computing in Science and Engineering
GPU-based parallel particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A comment on bio-inspired optimisation via GPU architecture: the genetic algorithm workload
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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This paper studies the impact of varying the population's size and the problem's dimensionality in a parallel implementation, for an NVIDIA GPU, of a canonical GA. The results show that there is an effective gain in the data parallel model provided by modern GPU's and enhanced by high level languages such as OpenCL. In the reported experiments it was possible to obtain a speedup higher than 140 thousand times for a population's size of 262 144 individuals.