Parallel hybrid genetic algorithms on consumer-level graphics hardware
Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006
Institute of Electrical and Electronics Engineers
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 pseudodeterministic selection method is also studied.
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Paper presented at the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 16-21 July 2006, Vancouver, Canada.
ISBN of the source publication: 9780780394872
Wong, M.-L., & Wong, T.-T. (2006). Parallel hybrid genetic algorithms on consumer-level graphics hardware. In Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 2973-2980). Piscataway: Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2006.1688683