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Enhanced Cooperative Co-evolution Genetic Algorithm for Rule-Based Pattern Classification

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Hybrid Artificial Intelligence Systems (HAIS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5271))

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Abstract

Genetic algorithms (GAs) have been widely used as soft computing techniques in various application domains, while cooperative co-evolution algorithms were proposed in the literature to improve the performance of basic GAs. In this paper, an enhanced cooperative co-evolution genetic algorithm (ECCGA) is proposed for rule-based pattern classification. Concurrent local and global evolution and conclusive global evolution are proposed to improve further the classification performance. Different approaches of ECCGA are evaluated on benchmark classification data sets, and the results show that ECCGA can achieve better performance than the cooperative co-evolution genetic algorithm and normal GA.

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Author information

Authors and Affiliations

  1. Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, 119615

    Fangming Zhu

  2. School of Engineering and Design, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

    Sheng-Uei Guan

Authors
  1. Fangming Zhu
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  2. Sheng-Uei Guan
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Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Ajith Abraham

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton,, T6G 2V4, Alberta, Canada

    Witold Pedrycz

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Zhu, F., Guan, SU. (2008). Enhanced Cooperative Co-evolution Genetic Algorithm for Rule-Based Pattern Classification. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_15

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  • DOI: https://doi.org/10.1007/978-3-540-87656-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87655-7

  • Online ISBN: 978-3-540-87656-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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