Learning and Evolution of Genetic Network Programming with Knowledge Transfer

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@InProceedings{Li:2014:CECd,
  title =        "Learning and Evolution of Genetic Network Programming
                 with Knowledge Transfer",
  author =       "Xianneng Li and Wen He and Kotaro Hirasawa",
  pages =        "798--805",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, Representation and operators,
                 Adaptive dynamic programming and reinforcement
                 learning",
  DOI =          "doi:10.1109/CEC.2014.6900315",
  abstract =     "Traditional evolutionary algorithms (EAs) generally
                 starts evolution from scratch, in other words,
                 randomly. However, this is computationally consuming,
                 and can easily cause the instability of evolution. In
                 order to solve the above problems, this paper describes
                 a new method to improve the evolution efficiency of a
                 recently proposed graph-based EA genetic network
                 programming (GNP) by introducing knowledge transfer
                 ability. The basic concept of the proposed method,
                 named GNP-KT, arises from two steps: First, it
                 formulates the knowledge by discovering abstract
                 decision-making rules from source domains in a learning
                 classifier system (LCS) aspect; Second, the knowledge
                 is adaptively reused as advice when applying GNP to a
                 target domain. A reinforcement learning (RL)-based
                 method is proposed to automatically transfer knowledge
                 from source domain to target domain, which eventually
                 allows GNP-KT to result in better initial performance
                 and final fitness values. The experimental results in a
                 real mobile robot control problem confirm the
                 superiority of GNP-KT over traditional methods.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Xianneng Li Wen He Kotaro Hirasawa

Citations