A modular genetic programming system

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Flasch:thesis,
  author =       "Oliver Flasch",
  title =        "A modular genetic programming system",
  school =       "Fakultaet fuer Informatik, Technische Universitaet
                 Dortmund",
  year =         "2015",
  address =      "Germany",
  month =        "6 " # may,
  keywords =     "genetic algorithms, genetic programming, genetische
                 programmierung, symbolic regression, symbolische
                 regression, data mining, computational intelligence,
                 big data",
  bibsource =    "OAI-PMH server at eldorado.uni-dortmund.de",
  contributor =  "Guenter Rudolph and",
  language =     "eng",
  oai =          "oai:eldorado.tu-dortmund.de:2003/34162",
  URL =          "https://eldorado.tu-dortmund.de/bitstream/2003/34162/1/Dissertation.pdf",
  URL =          "http://hdl.handle.net/2003/34162",
  DOI =          "doi:10.17877/DE290R-7807",
  size =         "208 pages",
  abstract =     "Genetic Programming (GP) is an evolutionary algorithm
                 for the automatic discovery of symbolic expressions,
                 e.g. computer programs or mathematical formulae, that
                 encode solutions to a user-defined task. Recent
                 advances in GP systems and computer performance made it
                 possible to successfully apply this algorithm to
                 real-world applications. This work offers three main
                 contributions to the state-of-the art in GP systems:
                 (I) The documentation of RGP, a state-of-the art GP
                 software implemented as an extension package to the
                 popular R environment for statistical computation and
                 graphics. GP and RPG are introduced both formally and
                 with a series of tutorial examples. As R itself, RGP is
                 available under an open source license. (II) A
                 comprehensive empirical analysis of modern GP
                 heuristics based on the methodology of Sequential
                 Parameter Optimisation. The effects and interactions of
                 the most important GP algorithm parameters are analysed
                 and recommendations for good parameter settings are
                 given. (III) Two extensive case studies based on
                 real-world industrial applications. The first
                 application involves process control models in steel
                 production, while the second is about meta-model-based
                 optimisation of cyclone dust separators. A comparison
                 with traditional and modern regression methods reveals
                 that GP offers equal or superior performance in both
                 applications, with the additional benefit of
                 understandable and easy to deploy models. Main
                 motivation of this work is the advancement of GP in
                 real-world application areas. The focus lies on a
                 subset of application areas that are known to be
                 practical for GP, first of all symbolic regression and
                 classification. It has been written with practitioners
                 from academia and industry in mind.",
  notes =        "Supervisor Thomas Bartz-Beielstein LS 11. RGP. Meta
                 Models for Cyclone Dust Separators (AppDust). Roll
                 Train Control Models (AppSteel)

                 Figure 3.1: Features versus costs of modern GP system
                 offerings: RGP ECJ DataModeler Eureqa DataModeler
                 tinyGP",
}

Genetic Programming entries for Oliver Flasch

Citations