Genetic Programming, Probabilistic Incremental Program Evolution, and Scalability

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{ondas:2005:WSC,
  author =       "Radovan Ondas and Martin Pelikan and Kumara Sastry",
  title =        "Genetic Programming, Probabilistic Incremental Program
                 Evolution, and Scalability",
  booktitle =    "WSC10: 10th Online World Conference on Soft Computing
                 in Industrial Applications",
  year =         "2005",
  editor =       "Joshua Knowles",
  pages =        "363--372",
  address =      "On the World Wide Web",
  month =        "19 " # sep # " - 7 " # oct,
  organisation = "World Federation of Soft Computing (WFSC), European
                 Neural Network Society (ENNS), North American Fuzzy
                 Information Processing Society (NAFIPS), European
                 Society for Fuzzy Logic and Technology (EUSFLAT), and
                 International Fuzzy Systems Association (IFSA)",
  email =        "ondasr@umsl.edu pelikan@cs.umsl.edu ksastry@uiuc.edu",
  keywords =     "genetic algorithms, genetic programming, scaling,
                 PIPE, scalability, order problem, trap problem",
  ISBN =         "3-540-29123-7",
  URL =          "http://isxp1010c.sims.cranfield.ac.uk/Papers/paper122.pdf",
  size =         "10 pages",
  abstract =     "We discuss scalability of standard genetic programming
                 (GP) and the probabilistic incremental program
                 evolution (PIPE). To investigate the need for both
                 effective mixing and linkage learning, two test
                 problems are considered: ORDER problem, which is rather
                 easy for any recombination-based GP, and TRAP or the
                 deceptive trap problem, which requires the algorithm to
                 learn interactions among subsets of terminals. The
                 scalability results show that both GP and PIPE scale up
                 polynomially with problem size on the simple ORDER
                 problem, but they both scale up exponentially on the
                 deceptive problem. This indicates that while standard
                 recombination is sufficient when no interactions need
                 to be considered, for some problems linkage learning is
                 necessary. These results are in agreement with the
                 lessons learned in the domain of binary-string genetic
                 algorithms (GAs). Furthermore, the paper investigates
                 the effects of introducing unnecessary and irrelevant
                 primitives on the performance of GP and PIPE.",
  notes =        "http://www.cranfield.ac.uk/wsc10/ broken Cf
                 \cite{1068310}.

                 Experimental, ORDER, TRAP, JOIN, NEG_JOIN, JUNK.
                 Linkage learning and EDAs should be applied to
                 GP.

                 http://isxp1010c.sims.cranfield.ac.uk/Presentations/presentation122.pdf
                 broken slides (1.3Mbyte)",
}

Genetic Programming entries for Radovan Ondas Martin Pelikan Kumara Sastry