Evolutionary System Identification: Modern Concepts and Practical Applications

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

  author =       "Stephan M. Winkler",
  title =        "Evolutionary System Identification: Modern Concepts
                 and Practical Applications",
  publisher =    "Trauner Verlag+Buchservice GmbH",
  year =         "2009",
  number =       "59",
  series =       "Johannes Kepler University, Linz, Reihe C",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-85499-569-2",
  URL =          "http://www.amazon.de/Evolutionary-System-Identification-Practical-Applications/dp/3854995695",
  broken =       "http://www.trauner.at/buchdetail.aspx?artnr=20134591",
  abstract =     "System identification denotes the data driven
                 generation of mathematical models for systems; the
                 result of a system identification algorithm consists in
                 a mathematical description of the behaviour of the
                 analysed system. Evolutionary computation is a subfield
                 of computational intelligence that uses concepts
                 inspired by natural evolution; one of the most famous
                 evolutionary techniques is the genetic algorithm, a
                 global optimisation technique using aspects inspired by
                 evolutionary biology such as selection, recombination,
                 mutation and inheritance. This thesis concentrates on
                 evolutionary system identification techniques based on
                 genetic programming (GP), an extension of the genetic
                 algorithm: Mathematical expressions are produced by an
                 evolutionary process that uses the given measurement
                 data. The first part of this thesis describes
                 theoretical concepts used in this work as well as our
                 GP implementation for the HeuristicLab framework.
                 Concepts for monitoring population dynamics during the
                 execution of the GP process are also described; we here
                 concentrate on genetic diversity and genetic
                 propagation. The application of advanced selection
                 principles and optimization stages is also explained as
                 well as on-line and sliding window GP variants. The
                 second part of this thesis summarises the results of
                 system identification test series; the data sets used
                 here include dynamic measurement data of mechatronical
                 systems as well as classification benchmark problems.
                 The results of these tests demonstrate the ability of
                 this method to produce models of high quality for
                 different kinds of machine learning problems, and also
                 give insights into population dynamic processes that
                 occur during the execution of a GP process.",
  notes =        "Dipl.-Ing. Dr. Stephan Winkler, Studium der Informatik
                 und Doktoratsstudium an der JKU in Linz. Bis 2006
                 wissenschaftlicher Mitarbeiter am LCM und am Institut
                 for Design und Regelung mechatronischer Systeme, danach
                 Anstellung im Rahmen des FWF Translational Research
                 Projekts L284 'GP-Based Techniques fort he Design
                 Virtual Sensors' an der FH Oeo, Campus Hagenberg. Ab
                 February 2009 Antritt einer Professur fuer
                 Bioinformatik an der FH Ooe.

                 Art. Nr. 20134591. See also \cite{Winkler:thesis}",
  size =         "422 pages",

Genetic Programming entries for Stephan M Winkler