Effectiveness of Multi-step Crossover Fusions in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Effectiveness of Multi-step Crossover Fusions in
                 Genetic Programming",
  author =       "Yoshiko Hanada and Nagahiro Hosokawa and Keiko Ono and 
                 Mitsuji Muneyasu",
  pages =        "2389--2396",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256564",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming,
                 Representation and operators, Discrete and
                 combinatorial optimization.",
  abstract =     "Multi-step Crossover Fusion (MSXF) and deterministic
                 MSXF (dMSXF) are promising crossover operators that
                 perform multi-step neighbourhood search between
                 parents, and applicable to various problems by
                 introducing a problem-specific neighbourhood structure
                 and a distance measure. Under their appropriate
                 definitions, MSXF and dMSXF can successively generate
                 offspring that acquire parents' good characteristics
                 along the path connecting the parents. In this paper,
                 we introduce MSXF and dMSXF to genetic programming
                 (GP), and apply them to symbolic regression problem. To
                 optimise trees, we define a neighbourhood structure and
                 its corresponding distance measure based on the largest
                 common subtree between parents with considering
                 ordered/unordered tree structures. Experiments using
                 symbolic regression problem instances showed the
                 effectiveness of a GP with the proposed MSXF and
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",

Genetic Programming entries for Yoshiko Hanada Nagahiro Hosokawa Keiko Ono Mitsuji Muneyasu