Using crossover based similarity measure to improve genetic programming generalization ability

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

@InProceedings{DBLP:conf/gecco/VanneschiG09,
  author =       "Leonardo Vanneschi and Steven Gustafson",
  title =        "Using crossover based similarity measure to improve
                 genetic programming generalization ability",
  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 =        "1139--1146",
  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:10.1145/1569901.1570054",
  abstract =     "Generalization is a very important issue in Machine
                 Learning. In this paper, we present a new idea for
                 improving Genetic Programming generalization ability.
                 The idea is based on a dynamic two-layered selection
                 algorithm and it is tested on a real-life drug
                 discovery regression application. The algorithm begins
                 using root mean squared error as fitness and the usual
                 tournament selection. A list of individuals called
                 ``repulsors'' is also kept in memory and initialized as
                 empty. As an individual is found to overfit the
                 training set, it is inserted into the list of
                 repulsors. When the list of repulsors is not empty,
                 selection becomes a two-layer algorithm: individuals
                 participating to the tournament are not randomly chosen
                 from the population but are themselves selected, using
                 the average dissimilarity to the repulsors as a
                 criterion to be maximized. Two kinds of
                 similarity/dissimilarity measures are tested for this
                 aim: the well known structural (or edit) distance and
                 the recently defined subtree crossover based similarity
                 measure. Although simple, this idea seems to improve
                 Genetic Programming generalization ability and the
                 presented experimental results show that Genetic
                 Programming generalizes better when subtree crossover
                 based similarity measure is used, at least for the test
                 problems studied in this paper.",
  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 Leonardo Vanneschi Steven M Gustafson

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