Novelty-based Fitness: An Evaluation under the Santa Fe Trail

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

@InProceedings{Doucette:2010:EuroGP,
  author =       "John Doucette and Malcolm Heywood",
  title =        "Novelty-based Fitness: An Evaluation under the Santa
                 Fe Trail",
  booktitle =    "Proceedings of the 13th European Conference on Genetic
                 Programming, EuroGP 2010",
  year =         "2010",
  editor =       "Anna Isabel Esparcia-Alcazar and Aniko Ekart and 
                 Sara Silva and Stephen Dignum and A. Sima Uyar",
  volume =       "6021",
  series =       "LNCS",
  pages =        "50--61",
  address =      "Istanbul",
  month =        "7-9 " # apr,
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-12147-0",
  DOI =          "doi:10.1007/978-3-642-12148-7_5",
  abstract =     "We present an empirical analysis of the effects of
                 incorporating novelty-based fitness (phenotypic
                 behavioral diversity) into Genetic Programming with
                 respect to training, test and generalization
                 performance. Three novelty-based approaches are
                 considered: novelty comparison against a finite archive
                 of behavioral archetypes, novelty comparison against
                 all previously seen behaviors, and a simple linear
                 combination of the first method with a standard fitness
                 measure. Performance is evaluated on the Santa Fe
                 Trail, a well known GP benchmark selected for its
                 deceptiveness and established generalization test
                 procedures. Results are compared to a standard
                 quality-based fitness function (count of food eaten).
                 Ultimately, the quality style objective provided better
                 overall performance, however, solutions identified
                 under novelty based fitness functions generally
                 provided much better test performance than their
                 corresponding training performance. This is interpreted
                 as representing a requirement for layered learning/
                 symbiosis when assuming novelty based fitness functions
                 in order to more quickly achieve the integration of
                 diverse behaviors into a single cohesive strategy.",
  notes =        "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
                 held in conjunction with EvoCOP2010 EvoBIO2010 and
                 EvoApplications2010",
}

Genetic Programming entries for John Doucette Malcolm Heywood

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