A Continuous Estimation of Distribution Algorithm by Evolving Graph Structures Using Reinforcement Learning

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@InProceedings{Li:2012:CECc,
  title =        "A Continuous Estimation of Distribution Algorithm by
                 Evolving Graph Structures Using Reinforcement
                 Learning",
  author =       "Xianneng Li and Bing Li and Shingo Mabu and 
                 Kotaro Hirasawa",
  pages =        "2097--2104",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256481",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming, Estimation of distribution
                 algorithms, Adaptive dynamic programming and
                 reinforcement learning, Representation and operators",
  abstract =     "A novel graph-based Estimation of Distribution
                 Algorithm (EDA) named Probabilistic Model Building
                 Genetic Network Programming (PMBGNP) has been proposed.
                 Inspired by classical EDAs, PMBGNP memorises the
                 current best individuals and uses them to estimate a
                 distribution for the generation of the new population.
                 However, PMBGNP can evolve compact programs by
                 representing its solutions as graph structures.
                 Therefore, it can solve a range of problems different
                 from conventional ones in EDA literature, such as data
                 mining and Reinforcement Learning (RL) problems. This
                 paper extends PMBGNP from discrete to continuous search
                 space, which is named PMBGNP-AC. Besides evolving the
                 node connections to determine the optimal graph
                 structures using conventional PMBGNP, Gaussian
                 distribution is used for the distribution of continuous
                 variables of nodes. The mean value mu and standard
                 deviation sigma are constructed like those of classical
                 continuous Population-based incremental learning
                 (PBILc). However, a RL technique, i.e., Actor-Critic
                 (AC), is designed to update the parameters (mu and
                 sigma). AC allows us to calculate the
                 Temporal-Difference (TD) error to evaluate whether the
                 selection of the continuous value is better or worse
                 than expected. This scalar reinforcement signal can
                 decide whether the tendency to select this continuous
                 value should be strengthened or weakened, allowing us
                 to determine the shape of the probability density
                 functions of the Gaussian distribution. The proposed
                 algorithm is applied to a RL problem, i.e., autonomous
                 robot control, where the robot's wheel speeds and
                 sensor values are continuous. The experimental results
                 show the superiority of PMBGNP-AC comparing with the
                 conventional algorithms.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Xianneng Li Bing Li Shingo Mabu Kotaro Hirasawa

Citations