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An Improved Co-Evolution Genetic Algorithm for Combinatorial Optimization Problems

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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

This paper presents an improved co-evolution genetic algorithm (ICGA), which uses the methodology of game theory to solve the mode deception and premature convergence problem. In ICGA, groups become different players in the game. Mutation operator is designed to simulate the situation in the evolutionary stable strategy. Information transfer mode is added to ICGA to provide greater decision-making space. ICGA is used to solve large-scale deceptive problems and an optimal control problem. Results of numerical tests validate the algorithm’s excellent performance.

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Author information

Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing, China

    Nan Li & Yi Luo

Authors
  1. Nan Li
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  2. Yi Luo
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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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Li, N., Luo, Y. (2011). An Improved Co-Evolution Genetic Algorithm for Combinatorial Optimization Problems. 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_60

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  • DOI: https://doi.org/10.1007/978-3-642-21515-5_60

  • 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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