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
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Parallelization of an evolutionary algorithm on a platform with multi-core processors
EA'09 Proceedings of the 9th international conference on Artificial evolution
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Towards cost-effective bio-inspired optimization: a prospective study on the GPU architecture
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper characterizes a genetic algorithm based on the analysis of the workload of its operators. Different granular parallel implementations of a genetic algorithm in the GPU architecture are compared against the correspondent sequential version. With the help of three benchmark problems, a complete characterization of the relative execution times of the genetic operators, varying the population cardinality and the genotype size, is offered. The best speedups, obtained with large populations, are higher than one thousand times faster than the corresponding sequential version. The assessment of different granularity levels shows that the two-dimensional parallelism supported by the GPU architecture is valuable for the crossover operator.