A comparison of predictive measures of problem difficulty for classification with Genetic Programming

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

  author =       "Leonardo Trujillo and Yuliana Martinez and 
                 Edgar Galvan-Lopez and Pierrick Legrand",
  title =        "A comparison of predictive measures of problem
                 difficulty for classification with Genetic
  booktitle =    "ERA 2012",
  year =         "2012",
  address =      "Tijuana, Mexico",
  month =        nov # " 14-16",
  organisation = "El Centro de Investigacion y Desarrollo de Tecnologia
                 Digital, CITEDI, Research and Development Center
                 Digital Technology",
  keywords =     "genetic algorithms, genetic programming, Performance
                 prediction, Classification",
  URL =          "http://hal.inria.fr/hal-00757363",
  URL =          "http://hal.inria.fr/docs/00/75/73/63/PDF/ERA_2012_NSC.pdf",
  bibsource =    "OAI-PMH server at hal.archives-ouvertes.fr",
  language =     "ENG",
  oai =          "oai:hal.inria.fr:hal-00757363",
  type =         "conference proceeding",
  size =         "12 pages",
  abstract =     "In the field of Genetic Programming (GP) a question
                 exists that is difficult to solve; how can problem
                 difficulty be determined? In this paper the overall
                 goal is to develop predictive tools that estimate how
                 difficult a problem is for GP to solve. Here we analyse
                 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. The
                 second 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 a GP system. These predictive variables
                 are domain specific thus problems are described in the
                 context of the problem domain. This paper compares an
                 EI, the Negative Slope Coefficient, and a PEP model for
                 a GP classifier. Results suggest that the EI does not
                 correlate with the performance of GP classifiers.
                 Conversely, the PEP models show a high correlation with
                 GP performance. It appears that while an EI estimates
                 the difficulty of a search, it does not necessarily
                 capture the difficulty of the underlying problem.
                 However, while PEP models treat GP as a computational
                 black-box, they can produce accurate performance
  notes =        "http://era.citedi.mx/site/",

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