Genetic Network Programming with generalized rule accumulation

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Lutao Wang and Shingo Mabu and Qingbiao Meng and 
                 Kotaro Hirasawa",
  title =        "Genetic Network Programming with generalized rule
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Network Programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "Genetic Network Programming(GNP) is a newly developed
                 evolutionary computation method using a directed graph
                 as its gene structure, which is its unique feature. It
                 is competent for dealing with complex problems in
                 dynamic environments and is now being well studied and
                 applied to many real-world problems such as: elevator
                 supervisory control, stock price prediction, traffic
                 volume forecast and data mining, etc. This paper
                 proposes a new method to accumulate evolutionary
                 experiences and guide agent's actions by extracting and
                 using generalised rules. Each generalized rule is a
                 state-action chain which contains the past information
                 and the current information. These generalised rules
                 are accumulated and updated in the evolutionary period
                 and stored in the rule pool which serves as an
                 experience set for guiding new agent's actions. We
                 designed a two-stage architecture for the proposed
                 method and applied it to the Tile-world problem, which
                 is an excellent benchmark for multi-agent systems. The
                 simulation results demonstrated the efficiency and
                 effectiveness of the proposed method in terms of both
                 generalisation ability and average fitness values and
                 showed that the generalised rule accumulation method is
                 especially remarkable when dealing with non-Markov
  DOI =          "doi:10.1109/CEC.2010.5586284",
  notes =        "WCCI 2010. Also known as \cite{5586284}",

Genetic Programming entries for Lutao Wang Shingo Mabu QingBiao Meng Kotaro Hirasawa