Titlebook: Massively Parallel Evolutionary Computation on GPGPUs; Shigeyoshi Tsutsui,Pierre Collet Book 2013 Springer-Verlag Berlin Heidelberg 2013 A

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Generic Local Search (Memetic) Algorithm on a Single GPGPU Chipf the EASEA platform provides an automatic generation and parallelization of an MA for both novice and experienced users. Experiments on a benchmark function and a real-world problem reveal speedups ranging between × 70 and × 120, depending on population size and number of local search iterations.

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Book 2013s such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively par

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Why GPGPUs for Evolutionary Computation?e more powerful chips, manufacturers then started developing multi-core processors, a path that had already been taken by graphics cards manufacturers earlier on. In 2012, NVIDIA came out with GK110 processors boasting 2,880 single precision cores and 960 double precision cores, for a computing powe

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arGA: Adaptive Resolution Micro-genetic Algorithm with Tabu Search to Solve MINLP Problems Using GPUProgramming (MINLP) and non-convex Nonlinear Programming (NLP) problems. The target is to solve MINLP/NLP problems from different domains of research in fewer fitness evaluations as compared to other state-of-the-art stochastic algorithms in the area. The algorithm is GPU compatible and shows remark

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