Study on Probabilistic Model Building Genetic Network Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{XiannengLi:thesis,
  author =       "Xianneng Li",
  title =        "Study on Probabilistic Model Building Genetic Network
                 Programming",
  school =       "Waseda University",
  year =         "2013",
  address =      "Japan",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, estimation of
                 distribution algorithm, genetic network programming",
  URL =          "http://hdl.handle.net/2065/40062",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40062/1/Honbun-6149.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40062/2/Shinsa-6149.pdf",
  URL =          "http://dspace.wul.waseda.ac.jp/dspace/bitstream/2065/40062/3/Gaiyo-6149.pdf",
  size =         "128 pages",
  abstract =     "Estimation of Distribution Algorithm (EDA) is one of
                 the most important branches in Evolutionary Computation
                 (EC). Different from the conventional Evolutionary
                 Algorithms (EAs) which use stochastic ways to simulate
                 the biological genetic operators, i.e., crossover and
                 mutation, for new population generation, EDA constructs
                 a probabilistic model using the techniques of
                 statistics and machine learning to estimate the
                 probability distribution of the current population, and
                 samples the model to generate the new population. By
                 explicitly estimating and recombining the good partial
                 solutions of the population, EDA has been successfully
                 proven to outperform conventional EAs by avoiding the
                 premature convergence and speeding up the evolution
                 process in many problems.

                 The primary objective of this thesis is to propose a
                 novel paradigm of EDA named Probabilistic Model
                 Building Genetic Network Programming (PMBGNP), where
                 the directed graph structure of a novel graph-based EA
                 called Genetic Network Programming (GNP) is used to
                 represent its individuals. Different from most of the
                 current EDAs proposed in string structure based Genetic
                 Algorithm (GA) and tree structure based Genetic
                 Programming (GP), the distinguished graph structure
                 allows PMBGNP to ensure higher expression ability. As a
                 result, a sort of problems can be explored and solved
                 efficiently and effectively comparing with the
                 conventional research in EDA literature.

                 To achieve this objective, contributions of this thesis
                 are presented on the following two aspects: algorithm
                 part and application part.

                 From the perspective of algorithm part, first, the
                 thesis proposes the high-level PMBGNP to use Maximum
                 Likelihood Estimation (MLE) to model the probability
                 distribution of the promising individuals. PMBGNP is
                 empirically studied to show the capability of speeding
                 up the evolution efficiency by the estimation of
                 probability distribution. Second, the thesis addresses
                 the issue of population diversity loss by theoretical
                 comparison with classical EDAs, and proposes a hybrid
                 algorithm to maintain the population diversity of
                 PMBGNP. Third, the integration of PMBGNP and
                 Reinforcement Learning (RL) is studied. Inspired by
                 behaviourist psychology, RL concerns with reinforcing
                 the growth of the individuals by learning their
                 experiences. The learning knowledge formulated by Q
                 values can be approximated and incorporated into the
                 probabilistic modelling of PMBGNP to improve the
                 performance by constructing a more accurate model.
                 Finally, PMBGNP is extended from discrete optimisation
                 problems to continuous optimization problems.

                 From the viewpoint of application part, most of the
                 current studies in EDA are carried out in the benchmark
                 problems of GA and GP, such as function optimisation
                 and symbolic regression. Therefore, to accomplish one
                 of the essential challenges of EDA for novel
                 applications, the thesis applies PMBGNP to two novel
                 applications of EDA, including data mining and the
                 problems of controlling the agents' behaviour. By
                 comparing with the other state-of-the-art algorithms,
                 PMBGNP is testified to be capable of achieving better
                 performances.",
}

Genetic Programming entries for Xianneng Li

Citations