Semantic Sub-tree Crossover Operator for Postfix Genetic Programming

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

@InProceedings{conf/bic-ta/DabhiC12,
  author =       "Vipul K. Dabhi and Sanjay Chaudhary",
  title =        "Semantic Sub-tree Crossover Operator for Postfix
                 Genetic Programming",
  booktitle =    "Proceedings of Seventh International Conference on
                 Bio-Inspired Computing: Theories and Applications
                 (BIC-TA 2012)",
  year =         "2012",
  editor =       "Jagdish Chand Bansal and Pramod Kumar Singh and 
                 Kusum Deep and Millie Pant and Atulya Nagar",
  volume =       "201",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "391--402",
  publisher_address = "India",
  publisher =    "Springer",
  language =     "English",
  keywords =     "genetic algorithms, genetic programming, Postfix
                 genetic programming, Symbolic regression, Empirical
                 modelling, Semantic sub-tree crossover operator",
  isbn13 =       "978-81-322-1037-5",
  bibdate =      "2013-01-16",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/bic-ta/bic-ta2012-1.html#DabhiC12",
  DOI =          "doi:10.1007/978-81-322-1038-2_33",
  abstract =     "Design of crossover operator plays a crucial role in
                 Genetic Programming (GP). The most studied issues
                 related to crossover operator in GP are: (1) ensuring
                 that crossover operator always produces syntactically
                 valid individuals (2) improving search efficiency of
                 crossover operator. These issues become crucial when
                 the individuals are represented using linear string
                 representation. This paper aims to introduce postfix GP
                 approach to symbolic regression for solving empirical
                 modelling problems. The main contribution includes (1)
                 a linear string (postfix notation) based genome
                 representation method and stack based evaluation to
                 reduce space-time complexity of GP algorithm (2)
                 ensuring that sub-tree crossover operator always
                 produces syntactically valid genomes in linear string
                 representation (3) using semantic information of
                 sub-trees, to be swapped, while designing crossover
                 operator for linear genome representation to provide
                 additional search guidance. The proposed method is
                 tested on two real valued symbolic regression problems.
                 Two different constant creation techniques for Postfix
                 GP, one that explicitly use list of constants and
                 another without use of the list, are presented to
                 evolve useful numeric constants for symbolic regression
                 problems. The results on tested problems show that
                 postfix GP comprised of semantic sub-tree crossover
                 offers a new possibility for efficiently solving
                 empirical modelling problems.",
}

Genetic Programming entries for Vipul K Dabhi Sanjay Chaudhary

Citations