NDLI: Parallel Hybrid Genetic Algorithms on Consumer-Level Graphics Hardware

Content Provider	IEEE Xplore Digital Library
Author	Man-Leung Wong Tien-Tsin Wong
Copyright Year	2006
Description	Author affiliation: Lingnan Univ., Tuen Mun (Man-Leung Wong)
Abstract	In this paper, we report a parallel hybrid genetic algorithm (HGA) on consumer-level graphics cards. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number generation procedure are performed in graphics processing unit (GPU) and thus our parallel HGA can be executed effectively and efficiently. We propose the pseudo-deterministic selection method which is comparable to the traditional global selection approach with significant execution time performance advantages. We perform experiments to compare our parallel HGA with our previous parallel FEP (fast evolutionary programming) and demonstrate that the former is much more effective and efficient than the latter. The parallel and sequential implementations of HGA are compared in a number of experiments, it is observed that the former outperforms the latter significantly. The effectiveness and efficiency of the pseudo-deterministic selection method is also studied.
Starting Page	2973
Ending Page	2980
File Size	670520
Page Count	8
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688683
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Hardware Genetic programming Genetic mutations Random number generation Computer graphics Central Processing Unit Parallel programming Rendering (computer graphics) Microcomputers
Content Type	Text
Resource Type	Article

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