Parallel hybrid genetic algorithms on consumer-level graphics hardware

Man-Leung Wong, Tien-Tsin Wong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

29 Citations (Scopus)

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 pseudodeterministic selection method is also studied.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2973-2980
Number of pages8
ISBN (Print)0780394879, 9780780394872
Publication statusPublished - 2006
Externally publishedYes
EventIEEE Congress on Evolutionary Computation 2006 - Vancouver, Canada
Duration: 16 Jul 200621 Jul 2006
https://ieeexplore.ieee.org/xpl/conhome/11108/proceeding (Proceedings)

Conference

ConferenceIEEE Congress on Evolutionary Computation 2006
Abbreviated titleIEEE CEC 2006
Country/TerritoryCanada
CityVancouver
Period16/07/0621/07/06
Internet address

Cite this