Discovering Rubik's Cube Subgroups using Coevolutionary GP -- A Five Twist Experiment

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Robert J. Smith and Stephen Kelly and 
                 Malcolm Heywood",
  title =        "Discovering Rubik's Cube Subgroups using
                 Coevolutionary GP -- A Five Twist Experiment",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "789--796",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908887",
  abstract =     "direct policy discovery (a form of reinforcement
                 learning) using genetic programming (GP) for the 3 by 3
                 by 3 Rubik's Cube. Specifically, a synthesis of two
                 approaches is proposed: 1) a previous group theoretic
                 formulation is used to suggest a sequence of objectives
                 for developing solutions to different stages of the
                 overall task; and 2) a hierarchical formulation of GP
                 policy search is used in which policies adapted for an
                 earlier objective are explicitly transferred to aid the
                 construction of policies for the next objective. The
                 resulting hierarchical organization of policies
                 explicitly demonstrates task decomposition and policy
                 reuse. Algorithmically, the process makes use of a
                 recursive call to a common approach for maintaining a
                 diverse population of GP individuals and then learns
                 how to reuse subsets of programs (policies) developed
                 against the earlier objective. Other than the two
                 objectives, we do not explicitly identify how to
                 decompose the task or mark specific policies for reuse.
                 Moreover, at the end of evolution we return a
                 population solving 100percent of 17,675,698 different
                 initial Cubes for the two objectives currently in
  notes =        "Dalhousie University

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",

Genetic Programming entries for Robert J Smith Stephen Kelly Malcolm Heywood