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Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation

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Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation.

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Author information

Authors and Affiliations

  1. School of Engineering, Nanyang Polytechnic, Singapore, 569830, Singapore

    Yong Wee Foo

  2. School of Engineering, University of Glasgow, Glasgow, G12 8LT, UK

    Yong Wee Foo, Cindy Goh, Lipton Chan & Yun Li

  3. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China

    Lin Li

  4. School of Computer Science and Network Security, Dongguan University of Technology, Songshanhu, 523808, Guangdong, China

    Lin Li & Yun Li

Authors
  1. Yong Wee Foo
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  2. Cindy Goh
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  3. Lipton Chan
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  4. Lin Li
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  5. Yun Li
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Corresponding author

Correspondence to Yun Li .

Editor information

Editors and Affiliations

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

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

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

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Foo, Y.W., Goh, C., Chan, L., Li, L., Li, Y. (2017). Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_40

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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