Genetic Network Programming with Rule Accumulation Considering Judgment Order

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Lutao Wang and Shingo Mabu and Fengming Ye and 
                 Kotaro Hirasawa",
  title =        "Genetic Network Programming with Rule Accumulation
                 Considering Judgment Order",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "3176--3182",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P044.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983346",
  abstract =     "Genetic Network Programming (GNP) is an evolutionary
                 algorithm derived form GA and GP. It can deal with
                 complex problems in dynamic environments efficiently
                 and effectively because of its directed graph
                 structure, reusability of nodes, and implicit memory
                 function. This paper proposed a new method to optimize
                 GNP algorithm by strengthening its exploitation ability
                 through extracting and using rules. In the former
                 research, the order of judgment node chain is ignored.
                 The basic idea of GNP with Rule Accumulation
                 Considering Judgment Order (GNP with RA) is to extract
                 rules with order having high fitness values from each
                 individual and store them in the pool every generation.
                 A rule is defined as a sequence of successive judgment
                 results and a processing node, which represents the
                 good experiences of the past behaviors. As a result,
                 the rule pool serves as an experience set of GNP
                 obtained in the evolution process. By extracting the
                 rules during the evolution period and then matching
                 them with the situations of the environment, we could
                 guide agents' behavior properly and get better
                 performance of the agents. In this paper, GNP with RA
                 is applied to the problem of determining agents'
                 behaviors and Tile-world was used as the simulation
                 environment in order to evaluate its effectiveness. The
                 simulation results demonstrate that GNP with RA could
                 have better performances than the conventional GNP
                 method both in the average fitness value and
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known
                 as \cite{4983346}",

Genetic Programming entries for Lutao Wang Shingo Mabu Fengming Ye Kotaro Hirasawa