Grammatical Evolution Guided by Reinforcement

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

  author =       "Jack Mario Mingo and Ricardo Aler",
  title =        "Grammatical Evolution Guided by Reinforcement",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "1475--1482",
  address =      "Singapore",
  month =        "25-28 " # sep,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, grammatical
  isbn13 =       "1-4244-1340-0",
  DOI =          "doi:10.1109/CEC.2007.4424646",
  abstract =     "Grammatical Evolution is an evolutionary algorithm
                 able to develop, starting from a grammar, programs in
                 any language. Starting from the point that individual
                 learning can improve evolution, in this paper it is
                 proposed an extension of Grammatical Evolution that
                 looks at learning by reinforcement as a learning method
                 for individuals. This way, it is possible to
                 incorporate the Baldwinian mechanism to the
                 evolutionary process. The effect is widened with the
                 introduction of the Lamarck hypothesis. The system is
                 tested in two different domains: a symbolic regression
                 problem and an even parity Boolean function. Results
                 show that for these domains, a system which includes
                 learning obtains better results than a grammatical
                 evolution basic system.",
  notes =        "Q tree

                 CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
  file =         "1738.pdf",
  bibsource =    "DBLP,

Genetic Programming entries for Jack Mario Mingo Ricardo Aler Mur