Scalable estimation-of-distribution program evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Moshe Looks",
  title =        "Scalable estimation-of-distribution program
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "1",
  isbn13 =       "978-1-59593-697-4",
  pages =        "539--546",
  address =      "London",
  URL =          "",
  DOI =          "doi:10.1145/1276958.1277072",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Estimation of
                 Distribution Algorithms, empirical Study, heuristics,
                 optimisation, representation",
  size =         "7 pages",
  abstract =     "I present a new estimation-of-distribution approach to
                 program evolution where distributions are not estimated
                 over the entire space of programs. Rather, a novel
                 representation-building procedure that exploits domain
                 knowledge is used to dynamically select program
                 subspaces for estimation over. This leads to a system
                 of demes consisting of alternative representations
                 (i.e. program subspaces) that are maintained
                 simultaneously and managed by the overall system.
                 Meta-optimising semantic evolutionary search (MOSES), a
                 program evolution system based on this approach, is
                 described, and its representation-building subcomponent
                 is analysed in depth. Experimental results are also
                 provided for the overall MOSES procedure that
                 demonstrate good scalability.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071

                 New initialisation scheme but disappointingly no big
                 performance boost. Parity (AND OR NOT) Mux6 Mux-11.
                 Semantic sampling. C++. Holman Elegant normal form (cf.
        ENF Catalan lil-gp.",

Genetic Programming entries for Moshe Looks