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A Novel Strategy to Control Population Diversity and Convergence for Genetic Algorithm

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10385))

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Abstract

Genetic algorithm (GA), an efficient evolutionary algorithm inspired from the science of genetics, attracts the worldwide attention for several decades. This paper tries to strengthen the search ability of the population in GA in the way of improving the distance among individuals by introducing a new solution updating strategy based on the theory of Cooperative Game. The simulation is done using fourteen benchmark functions, and the results demonstrate that this modified genetic algorithm works efficiently.

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Author information

Authors and Affiliations

  1. School of Infomation and Electronical Information, Tongji University, Shanghai, 201804, China

    Dongyang Li, Lei Wang & Qidi Wu

  2. Sino-German College of Applied Sciences, Tongji University, Shanghai, 201804, China

    Weian Guo & Yanfen Mao

Authors
  1. Dongyang Li
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  2. Weian Guo
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  3. Yanfen Mao
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  4. Lei Wang
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  5. Qidi Wu
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Corresponding author

Correspondence to Weian Guo .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Li, D., Guo, W., Mao, Y., Wang, L., Wu, Q. (2017). A Novel Strategy to Control Population Diversity and Convergence for Genetic Algorithm. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_39

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  • DOI: https://doi.org/10.1007/978-3-319-61824-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61823-4

  • Online ISBN: 978-3-319-61824-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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