Empirical investigations on parallel competent genetic algorithms

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Empirical investigations on parallel competent genetic algorithms

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 10th annual conference on Genetic and evolutionary computation table of contents

Atlanta, GA, USA

SESSION: Genetic algorithms papers table of contents

Pages 1073-1080

Year of Publication: 2008

ISBN:978-1-60558-130-9

Authors

Miwako Tsuji	Hokkaido University, Sapporo, Japan
Masaharu Munetomo	Hokkaido University, Sapporo, Japan
Kiyoshi Akama	Hokkaido University, Sapporo, Japan

Sponsors

ACM: Association for Computing Machinery
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

ACM New York, NY, USA

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ABSTRACT

This paper empirically investigates parallel competent genetic algorithms (cGAs) [4]. cGAs, such as BOA [21], LINCGA [15], D⁵-GA [28], can solve GA-difficult problems by automatically learning problem structure as gene linkage. Parallel implementation of cGAs can reduce computational cost due to the linkage learning and give us problem solving environments for a wide spectrum of real-world problems. Although some parallel cGAs have been proposed [16, 18, 19], the effect of the parallelizations has not been investigated enough. This paper empirically discusses the applicability and property of parallel cGAs, including a new parallel cGA, parallel D⁵-GA.

REFERENCES

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