Pruning Techniques for Mixed Ensembles of Genetic Programming Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Mauro Castelli and Ivo Goncalves and Luca Manzoni and 
                 Leonardo Vanneschi",
  title =        "Pruning Techniques for Mixed Ensembles of Genetic
                 Programming Models",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "52--67",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-77552-4",
  DOI =          "doi:10.1007/978-3-319-77553-1_4",
  abstract =     "The objective of this paper is to define an effective
                 strategy for building an ensemble of Genetic
                 Programming (GP) models. Ensemble methods are widely
                 used in machine learning due to their features: they
                 average out biases, they reduce the variance and they
                 usually generalize better than single models. Despite
                 these advantages, building ensemble of GP models is not
                 a well-developed topic in the evolutionary computation
                 community. To fill this gap, we propose a strategy that
                 blends individuals produced by standard syntax-based GP
                 and individuals produced by geometric semantic genetic
                 programming, one of the newest semantics-based method
                 developed in GP. In fact, recent literature showed that
                 combining syntactic and semantics could improve the
                 generalization ability of a GP model. Additionally, to
                 improve the diversity of the GP models used to build up
                 the ensemble, we propose different pruning criteria
                 that are based on correlation and entropy, a commonly
                 used measure in information theory. Experimental
                 results, obtained over different complex problems,
                 suggest that the pruning criteria based on correlation
                 and entropy could be effective in improving the
                 generalization ability of the ensemble model and in
                 reducing the computational burden required to build
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and

Genetic Programming entries for Mauro Castelli Ivo Goncalves Luca Manzoni Leonardo Vanneschi