A Local Search Approach to Genetic Programming for Binary Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Emigdio Z-Flores and Leonardo Trujillo and 
                 Oliver Schuetze and Pierrick Legrand",
  title =        "A Local Search Approach to Genetic Programming for
                 Binary Classification",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1151--1158",
  keywords =     "genetic algorithms, genetic programming, Integrative
                 Genetic and Evolutionary Computation",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754797",
  DOI =          "doi:10.1145/2739480.2754797",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In standard genetic programming (GP), a search is
                 performed over a syntax space defined by the set of
                 primitives, looking for the best expressions that
                 minimize a cost function based on a training set.
                 However, most GP systems lack a numerical optimization
                 method to fine tune the implicit parameters of each
                 candidate solution. Instead, GP relies on more
                 exploratory search operators at the syntax level. This
                 work proposes a memetic GP, tailored for binary
                 classification problems. In the proposed method, each
                 node in a GP tree is weighted by a real-valued
                 parameter, which is then numerically optimized using a
                 continuous transfer function and the Trust Region
                 algorithm is used as a local search method.
                 Experimental results show that potential classifiers
                 produced by GP are improved by the local searcher, and
                 hence the overall search is improved achieving
                 significant performance gains, that are competitive
                 with state-of-the-art methods on well-known
  notes =        "Also known as \cite{2754797} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Emigdio Z-Flores Leonardo Trujillo Oliver Schuetze Pierrick Legrand