Proceedings Article10.1109/CEC.2005.1554979
Parallel evolutionary algorithms on graphics processing unit
Man Leung Wong,Tien-Tsin Wong,Ka-Ling Fok +2 more
- 12 Dec 2005
- Vol. 3, pp 2286-2293
TL;DR: This paper proposes to implement a parallel EA on consumer-level graphics cards and performs experiments to compare this parallel EA with an ordinary EA and demonstrates that the former is much more effective than the latter.
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Abstract: Evolutionary algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuit 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 paper, we propose to implement a parallel EA on consumer-level graphics cards. 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 cards are available in ubiquitous personal computers and these computers are easy to use and manage, more people are able to use our parallel algorithm to solve their problems encountered in real-world applications.
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Citations
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- 26 Mar 2008
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Simon Harding,Wolfgang Banzhaf +1 more
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Hybrid genetic algorithms for feature selection
TL;DR: Experiments revealed that the proposed hybrid GA is superior to both a simple GA and sequential search algorithms, and showed better convergence properties compared to the classical GAs.