Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Efficient Parallel Implementation of Evolutionary Algorithms on GPGPU Cards
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Hybrid of genetic algorithm and local search to solve MAX-SAT problem using nVidia CUDA framework
Genetic Programming and Evolvable Machines
Swarm's flight: accelerating the particles using C-CUDA
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Markerless articulated human body tracking from multi-view video with GPU-PSO
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Evaluation of parallel particle swarm optimization algorithms within the CUDATM architecture
Information Sciences: an International Journal
GPU computation in bioinspired algorithms: a review
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Expert Systems with Applications: An International Journal
Solving very large instances of the scheduling of independent tasks problem on the GPU
Journal of Parallel and Distributed Computing
Two ports of a full evolutionary algorithm onto GPGPU
EA'11 Proceedings of the 10th international conference on Artificial Evolution
An improved approximate k-nearest neighbors nonlocal-means denoising method with GPU acceleration
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Parallel neighbourhood search on many-core platforms
International Journal of Computational Science and Engineering
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Fine-grained parallel genetic algorithm (FGPGA), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a FGPGA method based on GPU-acceleration, which maps parallel GA algorithm to texture-rendering on consumer-level graphics cards. The analytical results demonstrate that the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGPGA solution.