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Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit

Tien-Tsin Wong and Man Leung Wong
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
Parallel Evolutionary Computations, Studies in Computational Intelligence, 2006, Volume 22/2006, 133-155

@article{wong2006parallel,

   title={Parallel evolutionary algorithms on consumer-level graphics processing unit},

   author={Wong, T.T. and Wong, M.},

   journal={Parallel Evolutionary Computations},

   pages={133–155},

   year={2006},

   publisher={Springer}

}

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Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this chapter, we propose to implement a parallel EA on consumer-level Graphics Processing Unit (GPU). We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics processing units are already widely available and installed on oridinary personal computers and they are easy to use and manage, more people will be able to use our parallel algorithm to solve their problems encountered in real-world applications.
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