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An Orthogonal Genetic Algorithm with Multi-parent Multi-point Crossover for Knapsack Problem

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 952))

Abstract

According to the inherent feature of knapsack problem, a multi-parent multi-point crossover operation (MP2X) is proposed, which is implanted with orthogonal experimental design method. The aim of implementing orthogonal experimental design method to MP2X operation is to fully utilizing the inherent information from multiple component of multiple individuals. Based on MP2X operation and orthogonal design method, a genetic algorithm variant (MPXOGA) is proposed in this paper. The simulation results on classic knapsack instances show that MPXOGA is better than several other solvers, including Hybrid Genetic Algorithm (HGA), Greedy Genetic Algorithm (GGA), Greedy Binary Particle Swarm Optimization Algorithm (GBPSOA) and Very Greedy PSO (VGPSO) in the ability of finding optimal solution, the efficiency and the robustness.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (71772060, 61375066). We will express our awfully thanks to the Swarm Intelligence Research Team of BeiYou University.

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Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xinchao Zhao, Jiaqi Chen & Rui Li

  2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Dunwei Gong

  3. School of Economics and Management, North China Electric Power University, Beijing, 102206, China

    Xingmei Li

Authors
  1. Xinchao Zhao
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  2. Jiaqi Chen
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  3. Rui Li
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  4. Dunwei Gong
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  5. Xingmei Li
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Corresponding author

Correspondence to Xinchao Zhao .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

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

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Zhao, X., Chen, J., Li, R., Gong, D., Li, X. (2018). An Orthogonal Genetic Algorithm with Multi-parent Multi-point Crossover for Knapsack Problem. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_38

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  • DOI: https://doi.org/10.1007/978-981-13-2829-9_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2828-2

  • Online ISBN: 978-981-13-2829-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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