Models of Performance of Evolutionary Program Induction Algorithms Based on Indicators of Problem Difficulty

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

  author =       "Mario Graff and Riccardo Poli and Juan J. Flores",
  title =        "Models of Performance of Evolutionary Program
                 Induction Algorithms Based on Indicators of Problem
  journal =      "Evolutionary Computation",
  year =         "2013",
  volume =       "21",
  number =       "4",
  pages =        "533--560",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 program-induction algorithms, performance forecasting,
                 hardness measures, wind speed forecasting",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/EVCO_a_00096",
  size =         "28 pages",
  abstract =     "Modelling the behaviour of algorithms is the realm of
                 Evolutionary Algorithm theory. From a practitioner's
                 point of view, theory must provide some guidelines
                 regarding which algorithm/parameters to use in order to
                 solve a particular problem. Unfortunately, most
                 theoretical models of evolutionary algorithms are
                 difficult to apply to realistic situations. However, in
                 recent work (Graff and Poli, 2008, 2010), where we
                 developed a method to practically estimate the
                 performance of evolutionary program induction
                 algorithms (EPAs), we stated addressing this issue. The
                 method was quite general; however, it suffered from
                 some limitations: it required the identification of a
                 set of reference problems, it required hand picking a
                 distance measure in each particular domain, and the
                 resulting models were opaque being typically linear
                 combinations of one hundred features or more. In this
                 paper, we propose a significant improvement of this
                 technique that overcomes the three limitations of our
                 previous method. We achieve this through the use of a
                 novel set of features for assessing problem difficulty
                 for EPAs which are very general, essentially being
                 based on the notion of finite difference. To show the
                 capabilities or our technique and compare it with our
                 previous performance models, we create models for the
                 same two important classes of problems - symbolic
                 regression on rational functions and Boolean function
                 induction - used in our previous work. We model a
                 variety of EPAs. The comparison showed that for the
                 majority of the algorithms and problem classes, the new
                 method produced much simpler and more accurate models
                 than before. To further illustrate the practicality of
                 the technique and its generality (beyond EPAs), we have
                 also used it to predict the performance of both
                 auto-regressive models and EPAs on the problem of wind
                 speed forecasting, obtaining simpler and accurate
                 models that outperform in all cases our previous
                 performance models.",
  notes =        "Posted online on 8 Nov 2012. Cited by \cite{Graff2013}
                 Neurocomputing, doi:10.1016/j.neucom.2013.05.035",

Genetic Programming entries for Mario Graff Guerrero Riccardo Poli Juan J Flores