Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
A data parallel approach to genetic programming using programmable graphics hardware
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
Program optimization carving for GPU computing
Journal of Parallel and Distributed Computing
Accelerating evolutionary computation with graphics processing units
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
A new identification scheme based on the perceptrons problem
EUROCRYPT'95 Proceedings of the 14th annual international conference on Theory and application of cryptographic techniques
Experiments in parallel constraint-based local search
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Towards paradisEO-MO-GPU: a framework for GPU-based local search metaheuristics
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
GPU-Based multi-start local search algorithms
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
ParadisEO-MO-GPU: a framework for parallel GPU-based local search metaheuristics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Optimization problems are more and more complex and their resource requirements are ever increasing. Although metaheuristics allow to significantly reduce the computational complexity of the search process, the latter remains time-consuming for many problems in diverse domains of application. As a result, the use of GPU has been recently revealed as an efficient way to speed up the search. In this paper, we provide a new methodology to design and implement efficiently local search methods on GPU. The work has been experimented on the permuted perceptron problem and the experimental results show that the approach is very efficient especially for large problem instances.