Adaptive Genetic Programs via Reinforcement Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{downing:2001:gecco,
  title =        "Adaptive Genetic Programs via Reinforcement Learning",
  author =       "Keith L. Downing",
  pages =        "19--26",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and 
                 W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and 
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and 
                 Max H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, Reinforcement
                 Learning, Baldwin Effect, Lamarckianism, Hybrid
                 Adaptive Systems",
  ISBN =         "1-55860-774-9",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
  size =         "8 page",
  abstract =     "Reinforced Genetic Programming (RGP) enhances standard
                 tree-based genetic programming (GP) [7] with
                 reinforcement learning (RL)[11]. Essentially, leaf
                 nodes of GP trees become monitored action-selection
                 points, while the internal nodes form a decision tree
                 for classifying the current state of the problem
                 solver. Reinforcements returned by the problem solver
                 govern both fitness evaluation and intra-generation
                 learning of the proper actions to take at the selection
                 points. In theory, the hybrid RGP system hints of
                 mutual benefits to RL and GP in controller-design
                 applications, by, respectively, providing proper
                 abstraction spaces for RL search, and accelerating
                 evolutionary progress via Baldwinian or Lamarckian
                 mechanisms. In practice, we demonstrate RGP's
                 improvements over standard GP search on maze-search
                 tasks",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of \cite{spector:2001:GECCO}",
}

Genetic Programming entries for Keith L Downing

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