Genetic algorithm with adaptive elitism-based immigrants for dynamic optimization problems

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Genetic algorithm with adaptive elitism-based immigrants for dynamic optimization problems

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

POSTER SESSION: Track 9: genetic algorithms table of contents

Pages 1865-1866

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Seung-Kyu Lee	Seoul National University, Seoul, South Korea
Byung-Ro Moon	Seoul National University, Seoul, South Korea

Sponsors

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

Publisher

ACM New York, NY, USA

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ABSTRACT

We propose a genetic algorithm with adaptive elitism-based immigrants which tunes the balance between elitism-based immigrants and random immigrants by itself. Experimental results show that our genetic algorithm with adaptive elitism-based immigrants performs better than that with the elitism-based immigrants for onemax and produces comparable results for royal road and knapsack problems.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

Shengxiang Yang, Genetic Algorithms with Elitism-Based Immigrants for Changing Optimization Problems, Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing, April 11-13, 2007, Valencia, Spain [doi>10.1007/978-3-540-71805-5_69]

INDEX TERMS

Primary Classification:
J. Computer Applications
J.m MISCELLANEOUS