A comparative study of an evolvability indicator and a predictor of expected performance for genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Leonardo Trujillo and Yuliana Martinez and 
                 Edgar Galvan Lopez and Pierrick Legrand",
  title =        "A comparative study of an evolvability indicator and a
                 predictor of expected performance for genetic
  booktitle =    "GECCO Companion '12: Proceedings of the fourteenth
                 international conference on Genetic and evolutionary
                 computation conference companion",
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, Genetic programming: Poster",
  pages =        "1489--1490",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2331006",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "An open question within Genetic Programming (GP) is
                 how to characterize problemdifficulty. The goal is to
                 develop predictive tools that estimate how difficult a
                 problemis for GP to solve. Here we consider two groups
                 of methods. We call the first group Evolvability
                 Indicators (EI), measures that capture how amendable
                 the fitness landscape is to a GP search. Examples of
                 EIs are Fitness Distance Correlation (FDC) and Negative
                 Slope Coefficient (NSC). The second group are
                 Predictors of Expected Performance (PEP), models that
                 take as input a set of descriptive attributes of a
                 problem and predict the expected performance of GP.
                 This paper compares an EI, the NSC, and a PEP model for
                 a GP classifier. Results suggest that the EI does not
                 correlate with the performance of the GP classifiers.
                 Conversely, the PEP models show a high correlation with
                 GP performance.",
  notes =        "Also known as \cite{2331006} Distributed at

                 ACM Order Number 910122.",

Genetic Programming entries for Leonardo Trujillo Yuliana Martinez Edgar Galvan Lopez Pierrick Legrand