Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
GPU-based parallel particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle swarm optimization with triggered mutation and its implementation based on GPU
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
GPU-based island model for evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
CUDA by Example: An Introduction to General-Purpose GPU Programming
CUDA by Example: An Introduction to General-Purpose GPU Programming
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Speeding up a chaos-based image encryption algorithm using GPGPU
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Central force optimization on a GPU: a case study in high performance metaheuristics
The Journal of Supercomputing
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Diverse technologies have been used to accelerate the execution of Evolutionary Algorithms. Nowadays, the GPGPUcards have demonstrated a high efficiency in the improvement of the execution times in a wide range of scientific problems, including some excellent examples with diverse categories of Evolutionary Algorithms. Nevertheless, the studies in depth of the efficiency of each one of these technologies, and how they affect to the final performance are still scarce. These studies are relevant in order to reduce the execution time budget, and therefore affront higher dimensional problems. In this work, the improvement of the speed-up face to the percentage of threads used per block in the GPGPU card is analysed. The results conclude that a correct election of the occupancy --number of the threads per block-- contributes to win an additional speed-up.