Comparison of Discipulus Linear Genetic Programming Software with Support Vector Machines, Classification Trees, Neural Networks and Human Experts

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@TechReport{Deschaine:Discipulus_Comparison,
  author =       "Larry M. Deschaine and Frank D. Francone",
  title =        "Comparison of Discipulus Linear Genetic Programming
                 Software with Support Vector Machines, Classification
                 Trees, Neural Networks and Human Experts",
  type =         "White Paper",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming, SVM, ANN, DT",
  URL =          "http://www.rmltech.com/doclink/Comparison.White.Paper.pdf",
  abstract =     "Discipulus is multiple-run, linear,
                 genetic-programming software. Various versions have
                 been available commercially since 1998 (see,
                 www.aimlearning.com). Discipulus creates models
                 directly from data, like neural networks or support
                 vector machines.

                 This white paper reports on the result of a multi-year
                 study of the performance of Discipulus by Science
                 Applications International Corp (SAIC) and RML
                 Technologies, Inc. This study compared Discipulus to
                 several other powerful modelling tools on a wide
                 variety of industrial problems including regression and
                 classification problems, CRM problems, time series
                 problems, complex signal discrimination problems and
                 others.

                 We compared the modeling capability of Discipulus to
                 the following competitive modelling
                 technologies:

                 Vapnick Statistical Learning,

                 Neural Networks,

                 Decision Trees, and

                 Rule-Based Systems.

                 In brief summary, the other modelling tools performed
                 inconsistently sometimes they produced very good
                 results and sometimes mediocre or even very poor
                 results. None of these tools produced high quality
                 results across the board. In contrast, Discipulus (at
                 its default settings) always produced results that were
                 the same as or better than the best results from other
                 modelling techniques.

                 The results described in this white paper have all been
                 previously published in peer-reviewed scientific
                 publications.",
  size =         "16 pages",
}

Genetic Programming entries for Larry M Deschain Frank D Francone

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