Evolving genetic programming classifiers with novelty search

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Enrique Naredo and Leonardo Trujillo and 
                 Pierrick Legrand and Sara Silva and Luis Munoz",
  title =        "Evolving genetic programming classifiers with novelty
  journal =      "Information Sciences",
  year =         "2016",
  volume =       "369",
  pages =        "347--367",
  keywords =     "genetic algorithms, genetic programming, Novelty
                 search, Behaviour-based Search, Supervised
                 classification, Bloat",
  ISSN =         "0020-0255",
  URL =          "http://www.sciencedirect.com/science/article/pii/S002002551630473X",
  DOI =          "doi:10.1016/j.ins.2016.06.044",
  abstract =     "Novelty Search (NS) is a unique approach towards
                 search and optimization, where an explicit objective
                 function is replaced by a measure of solution novelty.
                 However, NS has been mostly used in evolutionary
                 robotics while its usefulness in classic machine
                 learning problems has not been explored. This work
                 presents a NS-based genetic programming (GP) algorithm
                 for supervised classification. Results show that NS can
                 solve real-world classification tasks, the algorithm is
                 validated on real-world benchmarks for binary and
                 multiclass problems. These results are made possible by
                 using a domain-specific behaviour descriptor. Moreover,
                 two new versions of the NS algorithm are proposed,
                 Probabilistic NS (PNS) and a variant of Minimal
                 Criteria NS (MCNS). The former models the behavior of
                 each solution as a random vector and eliminates all of
                 the original NS parameters while reducing the
                 computational overhead of the NS algorithm. The latter
                 uses a standard objective function to constrain and
                 bias the search towards high performance solutions. The
                 paper also discusses the effects of NS on GP search
                 dynamics and code growth. Results show that NS can be
                 used as a realistic alternative for supervised
                 classification, and specifically for binary problems
                 the NS algorithm exhibits an implicit bloat control

Genetic Programming entries for Enrique Naredo Leonardo Trujillo Pierrick Legrand Sara Silva Luis Munoz Delgado