Using Genetic Programming in Nonlinear Model Identification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InCollection{1793,
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author = "Stephan M. Winkler and Michael Affenzeller and
Stefan Wagner and Gabriel K. Kronberger and Michael Kommenda",
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title = "Using Genetic Programming in Nonlinear Model
Identification",
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booktitle = "Workshop on Identification in Automotive 2010",
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publisher = "Springer",
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year = "2010",
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editor = "Daniel Alberer and Hakan Hjalmarsson and
Luigi {del Re}",
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volume = "418",
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series = "Lecture Notes in Control and Information Sciences",
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chapter = "6",
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pages = "89--109",
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address = "Linz, Austria",
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month = jul,
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4471-2221-0",
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URL = "https://link.springer.com/chapter/10.1007/978-1-4471-2221-0_6",
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DOI = "doi:10.1007/978-1-4471-2221-0_6",
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abstract = "In this paper we summarize the use of genetic
programming (GP) in nonlinear system identification:
After giving a short introduction to evolutionary
computation and genetic algorithms, we describe the
basic principles of genetic programming and how it is
used for data based identification of nonlinear
mathematical models. Furthermore, we summarize projects
in which we have successfully applied GP in Research
and Development projects in the last years; we also
give a summary of several algorithmic enhancements that
have been successfully researched in the last years
(including offspring selection, on-line and sliding
window GP, operators for monitoring genetic process
dynamics, and the design of cooperative evolutionary
data mining agents). A short description of
HeuristicLab (HL), the optimization framework developed
by the HEAL research group, and the use of the GP
implementations in HL are given in the appendix of this
paper.",
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notes = "Published 2012? LNCIS, volume 418",
- }
Genetic Programming entries for
Stephan M Winkler
Michael Affenzeller
Stefan Wagner
Gabriel Kronberger
Michael Kommenda
Citations