Comparison of methods to compute simple genetic algorithms in GPU
Abstract
simple genetic algorithms can be used to search various troubleshooting. We present five different methods of parallel implementation in GPU for these algorithms. Speedups obtained maximum between 6% and 12.5% compared to the serial implementation.
Keywords:
Algorithms Parallel and Distributed, Parallel Applications to Problems of Real Solutions, Dedicated architectures and Specific, Evaluation, Performance Measurement and Prediction
References
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Styblinski, M. A. and Tang, T.-S. (1990). Experiments in nonconvex optimization: Stochastic approximation with function smoothing and simulated annealing. In Neural Networks 3, pages 467–483.
Styblinski, M. A. and Tang, T.-S. (1990). Experiments in nonconvex optimization: Stochastic approximation with function smoothing and simulated annealing. In Neural Networks 3, pages 467–483.
Published
2020-04-15
How to Cite
DE ANDRADE, Vinícius C. Oliveira; ZOLA, Wagner M. Nunan.
Comparison of methods to compute simple genetic algorithms in GPU. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SOUTHERN BRAZIL (ERAD-RS), 20. , 2020, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 155-156.
ISSN 2595-4164.
DOI: https://doi.org/10.5753/eradrs.2020.10784.
