Pursuing the Pareto Paradigm Tournaments, Algorithm Variations \& Ordinal Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

  author =       "Mark Kotanchek and Guido Smits and 
                 Ekaterina Vladislavleva",
  title =        "Pursuing the Pareto Paradigm Tournaments, Algorithm
                 Variations \& Ordinal Optimization",
  booktitle =    "Genetic Programming Theory and Practice {IV}",
  year =         "2006",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  volume =       "5",
  series =       "Genetic and Evolutionary Computation",
  pages =        "167--185",
  address =      "Ann Arbor",
  month =        "11-13 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-387-33375-4",
  DOI =          "doi:10.1007/978-0-387-49650-4_11",
  abstract =     "The ParetoGP algorithm, which adopts a multi-objective
                 optimisation approach to balancing expression
                 complexity and accuracy, has proven to have significant
                 impact on symbolic regression of industrial data due to
                 its improvement in speed and quality of model
                 development as well as user model selection.

                 In this chapter, we explore a range of topics related
                 to exploiting the Pareto paradigm. First we describe
                 and explore the strengths and weaknesses of the
                 ClassicGP and ParetoGP variants for symbolic regression
                 as well as touch on related algorithms.

                 Next, we show a derivation for the selection intensity
                 of tournament selection with multiple winners (albeit,
                 in a single-objective case). We then extend classical
                 tournament and elite selection strategies into a
                 multi-objective framework which allows classical GP
                 schemes to be readily Pareto-aware.

                 Finally, we introduce the latest extension of the
                 Pareto paradigm which is the melding with ordinal
                 optimization. It appears that ordinal optimisation will
                 provide a theoretical foundation to guide algorithm
                 design. Application of these insights has already
                 produced at least a four-fold improvement in the
                 performance for a suite of test problems.",
  notes =        "part of \cite{Riolo:2006:GPTP} Published Jan 2007
                 after the workshop


Genetic Programming entries for Mark Kotanchek Guido F Smits Ekaterina (Katya) Vladislavleva