Program optimization by random tree sampling

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

@InProceedings{DBLP:conf/gecco/TanjiI09,
  author =       "Makoto Tanji and Hitoshi Iba",
  title =        "Program optimization by random tree sampling",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1131--1138",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  doi =          "doi:10.1145/1569901.1570053",
  abstract =     "This paper describes a new program evolution method
                 named PORTS (Program Optimization by Random Tree
                 Sampling) which is motivated by the idea of
                 preservation and control of tree fragments. We
                 hypothesize that to reconstruct building blocks
                 efficiently, tree fragments of any size should be
                 preserved into the next generation, according to their
                 differential fitnesses. PORTS creates a new individual
                 by sampling from the promising trees by traversing and
                 transition between trees instead of subtree crossover
                 and mutation. Because the size of a fragment preserved
                 during a generation update follows a geometric
                 distribution, merits of the method are that it is
                 relatively easy to predict the behavior of tree
                 fragments over time and to control sampling size, by
                 changing a single parameter. Our experimental results
                 on three benchmark problems show that the performance
                 of PORTS is competitive with SGP (Simple Genetic
                 Programming). And we observed that there is a
                 significant difference of fragment distribution between
                 PORTS and simple GP.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Makoto Tanji Hitoshi Iba