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A Novel Multi-Population Genetic Algorithm for Multiple-Choice Multidimensional Knapsack Problems

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Advances in Computation and Intelligence (ISICA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6382))

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Abstract

In this paper, a novel Multi-Population Genetic Algorithm (MPGA) is proposed to solve the Multiple-choice Multidimensional Knapsack Problem (MMKP), a kind of classical combinatorial optimization problems. The proposed MPGA has two evolutionary populations and one archive population, and can effectively balance the search biases between the feasible space and the infeasible space. The experiment results demonstrate that the proposed MPGA is better than the existing algorithms, especially when the strength of constraints is relatively strong.

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Author information

Authors and Affiliations

  1. Nature Inspired Computation and Applications Laboratory, School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Qian Zhou & Wenjian Luo

  2. Anhui Key Laboratory of Software in Computing and Communication, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Qian Zhou & Wenjian Luo

Authors
  1. Qian Zhou
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  2. Wenjian Luo
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Editor information

Editors and Affiliations

  1. School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  2. School of Computer Science, China University of Geosciences, Wu-Han, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Chengyu Hu

  3. Computation Center, Wuhan University, 430072, Wuhan, Hubei, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, 965-8580, Aizu-Wakamatsu City, Fukushima, Japan

    Yong Liu

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© 2010 Springer-Verlag Berlin Heidelberg

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Zhou, Q., Luo, W. (2010). A Novel Multi-Population Genetic Algorithm for Multiple-Choice Multidimensional Knapsack Problems. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-16493-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16492-7

  • Online ISBN: 978-3-642-16493-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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