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Dynamic Crossover and Mutation Genetic Algorithm Based on Expansion Sampling

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Artificial Intelligence and Computational Intelligence (AICI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5855))

Abstract

The traditional genetic algorithm gets in local optimum easily, and its convergence rate is not satisfactory. So this paper proposed an improvement, using dynamic cross and mutation rate cooperate with expansion sampling to solve these two problems. The expansion sampling means the new individuals must compete with the old generation when create new generation, as a result, the excellent half ones are selected into the next generation. Whereafter several experiments were performed to compare the proposed method with some other improvements. The results are satisfactory. The experiment results show that the proposed method is better than other improvements at both precision and convergence rate.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, 1239 Si Ping Road, Shanghai, 200092, China

    Min Dong & Yan Wu

Authors
  1. Min Dong
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  2. Yan Wu
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Editor information

Editors and Affiliations

  1. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  2. College of Metrological Technology and Engineering, China Jiliang University, 310018, Hangzhou, Zhejiang Province, China

    Lanzhou Wang

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China

    Fu Lee Wang

  4. College of Information Science and Technology, Hainan University, 570228, Haikou, China

    Jingsheng Lei

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© 2009 Springer-Verlag Berlin Heidelberg

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Dong, M., Wu, Y. (2009). Dynamic Crossover and Mutation Genetic Algorithm Based on Expansion Sampling. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_16

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  • DOI: https://doi.org/10.1007/978-3-642-05253-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05252-1

  • Online ISBN: 978-3-642-05253-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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