Learning Grammar Rules in Probabilistic Grammar-Based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{conf/tpnc/WongWL16,
  author =       "Pak-Kan Wong and Man Leung Wong and Kwong-Sak Leung",
  title =        "Learning Grammar Rules in Probabilistic Grammar-Based
                 Genetic Programming",
  booktitle =    "Theory and Practice of Natural Computing - 5th
                 International Conference, {TPNC} 2016, Sendai, Japan,
                 December 12-13, 2016, Proceedings",
  editor =       "Carlos Martin-Vide and Takaaki Mizuki and 
                 Miguel A. Vega-Rodriguez",
  year =         "2016",
  volume =       "10071",
  isbn13 =       "978-3-319-49000-7",
  pages =        "208--220",
  series =       "Lecture Notes in Computer Science",
  keywords =     "genetic algorithms, genetic programming, estimation of
                 distribution programming adaptive grammar Bayesian
                 network",
  bibdate =      "2017-05-21",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/https://doi.org/10.1007/978-3-319-49001-4_17;
                 DBLP,
                 http://dblp.uni-trier.de/db/conf/tpnc/tpnc2016.html#WongWL16",
  DOI =          "doi:10.1007/978-3-319-49001-4_17",
  abstract =     "Grammar-based Genetic Programming (GBGP) searches for
                 a computer program in order to solve a given problem.
                 Grammar constrains the set of possible programs in the
                 search space. It is not obvious to write an appropriate
                 grammar for a complex problem. Our proposed Bayesian
                 Grammar-Based Genetic Programming with Hierarchical
                 Learning (BGBGP-HL) aims at automatically designing new
                 rules from existing relatively simple grammar rules
                 during evolution to improve the grammar structure. The
                 new grammar rules also reflects the new understanding
                 of the existing grammar under the given fitness
                 evaluation function. Based on our case study in
                 asymmetric royal tree problem, our evaluation shows
                 that BGBGP-HL achieves the best performance among the
                 competitors. Compared to other algorithms, search
                 performance of BGBGP-HL is demonstrated to be more
                 robust against dependencies and the changes in
                 complexity of programs.",
}

Genetic Programming entries for Pak-Kan Wong Man Leung Wong Kwong-Sak Leung

Citations