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Atavistic Strategy for Genetic Algorithm

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Advances in Swarm Intelligence (ICSI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6728))

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Abstract

Atavistic evolutionary strategy for genetic algorithm is put forward according to the atavistic phenomena existing in the process of biological evolution, and the framework of the new strategy is given also. The effectiveness analysis of the new strategy is discussed by three characteristics of the reproduction operators. The introduction of atavistic evolutionary strategy is highly compatible with the minimum induction pattern, and increases the population diversity to a certain extent. The experimental results show that the new strategy improves the performance of genetic algorithm on convergence time and solution quality.

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Author information

Authors and Affiliations

  1. Center of Information and Education Technology, Foshan University, Foshan, 528000, China

    Dongmei Lin

  2. School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

    Xiaodong Li

  3. Department of Computer Science and Technology, Foshan University, Foshan, 528000, China

    Dong Wang

Authors
  1. Dongmei Lin
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  2. Xiaodong Li
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  3. Dong Wang
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China

    Yuhui Shi

  3. Automation College, Chongqing University, 400030, Chongqing, China

    Yi Chai

  4. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, P.R. China

    Guoyin Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Lin, D., Li, X., Wang, D. (2011). Atavistic Strategy for Genetic Algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_59

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  • DOI: https://doi.org/10.1007/978-3-642-21515-5_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21514-8

  • Online ISBN: 978-3-642-21515-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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