A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA
Department of Computer Science, FEECS, VSB Technical University of Ostrava, Ostrava, Czech Republic
IEEE Third World Congress on Nature and Biologically Inspired Computing (NaBIC 2011), pp. 516-521, 2011
@article{kromer2011comparison,
title={A Comparison of Many-threaded Differential Evolution and Genetic Algorithms on CUDA},
author={Kr{"o}mer, P. and Plato{v{s}}, J. and Sn{‘a}{v{s}}el, V. and Abraham, A. and FEECS, V.S.B.},
year={2011}
}
The recent time has seen the rise of consumer grade massively parallel environments. Powerful GPUs and multi-core processors became widely available and easy to use programming APIs such as nVidia CUDA, OpenCL, and DirectCompute simplify the development of applications that can utilize them. In this environment, the nature inspired metaheuristics can be in suitable cases implemented in parallel without additional costs. Backed by the power of modern GPGPUs, the meta-heuristics can be deployed to solve practical real world problems. In this paper, we compare differential evolution and genetic algorithms implemented on CUDA when solving the independent tasks scheduling problem.
November 2, 2011 by hgpu
