Models of the Performance of Evolutionary Program Induction Algorithms

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

  author =       "Mario Graff-Guerrero",
  title =        "Models of the Performance of Evolutionary Program
                 Induction Algorithms",
  school =       "Department of Computing Science and Electronic
                 Engineering, University of Essex",
  year =         "2010",
  address =      "UK",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Cartesian Genetic Programming",
  URL =          "",
  size =         "142 pages",
  abstract =     "Evolutionary computation techniques have seen a
                 considerable popularity as problem solving and
                 optimisation tools in recent years. Theoreticians have
                 developed a variety of both exact and approximate
                 models of evolutionary algorithms. However, these
                 models are often criticised for being only applicable
                 to simplistic problems or algorithms with unrealistic

                 Here, we start rectifying this situation in relation to
                 what matters the most to practitioners and users of
                 program induction systems: performance. That is, we
                 introduce some simple and practical models for the
                 performance of program-induction algorithms. To test
                 our approach, we consider two important classes of
                 problems, symbolic regression and Boolean function
                 induction, and we model different versions of Genetic
                 Programming, Gene Expression Programming, Cartesian
                 Genetic Programming and Stochastic Iterated Hill

                 In all cases our models are able to accurately predict
                 the performance of each algorithm on unseen problems.
                 This allows, for example, the use of our models to
                 solve the algorithm selection problem (i.e., the
                 problem of deciding which is the best algorithm to
                 solve a problem) for program induction. Besides
                 performing accurate predictions, we show that our
                 models can help in the analysis and comparison of
                 different algorithms and/or algorithms with different
                 parameters setting. This process, too, can be
                 automatised. We illustrate this via the automatic
                 construction of a taxonomy for the stochastic program
                 induction algorithms considered in this study.

                 Although our approach was initially aimed at modelling
                 evolutionary program induction algorithms, it is in
                 fact very general and, in principle, can be used to
                 predict the performance of non-evolutionary learning
                 algorithms and problem solvers. To illustrate this, we
                 modelled one well-known training algorithm for
                 artificial neural networks and two common heuristics of
                 the off-line bin packing problem with very encouraging
  notes =        "supervisor Riccardo Poli",

Genetic Programming entries for Mario Graff Guerrero