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Adopting gene expression programming to generate extension strategies for incompatible problem

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Abstract

Exploring solving strategies for incompatible problem is a challenging task. Although extension strategy generating method (ESGM) can address incompatible problem by expanding reasoning and extension transformations, the solving process often suffers a combination explosion of computational cost. In order to overcome this shortcoming, a new approach to performing extension transformations based on gene expression programming (GEP) is proposed. The method is able to establish superior operations of extension transformations heuristically and iteratively, which avoids the combination explosion effectively. In order to make GEP adapt to such applying requirement, chromosome architecture, decoding mode, individual selection and convergence criteria are restudied. The proposed method is illustrated with the application of ESGM to a self-guided touring route design problem. Numerical results verify that the proposed method helps provide extension strategies efficiently and has a huge potential for more complex incompatible problem solving.

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Acknowledgments

This paper is sponsored by National Natural Science Foundation Project (61503085); National Natural Science Foundation Project (61273306); Science and Technology Planning Project of Guangdong Province (2012B061000012); and “Strengthening school by innovation” Project from Department of Education of Guangdong Province (261555116).

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Authors and Affiliations

  1. Research Institute of Extenics and Innovation Method, Guangdong University of Technology, Guangzhou, 510006, China

    Long Tang & Chunyan Yang

  2. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Weihua Li

Authors
  1. Long Tang
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  2. Chunyan Yang
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  3. Weihua Li
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Correspondence to Long Tang.

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Tang, L., Yang, C. & Li, W. Adopting gene expression programming to generate extension strategies for incompatible problem. Neural Comput & Applic 28, 2649–2664 (2017). https://doi.org/10.1007/s00521-016-2211-1

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  • DOI: https://doi.org/10.1007/s00521-016-2211-1

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