Using genetic programming in nonlinear model identification

Publikation: Beitrag in Buch/Bericht/TagungsbandKapitelBegutachtung

4 Zitate (Scopus)

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 R&D 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.

OriginalspracheEnglisch
TitelIdentification for Automotive Systems
Redakteure/-innenDaniel Alberer, Luigi del Re, Hakan Hjalmarsson
Herausgeber (Verlag)Springer Vieweg
Seiten89-109
Seitenumfang21
ISBN (Print)9781447122203
DOIs
PublikationsstatusVeröffentlicht - 2012
VeranstaltungWorkshop on Identification in Automotive - Linz, Österreich
Dauer: 15 Jul 201016 Jul 2010

Publikationsreihe

NameLecture Notes in Control and Information Sciences
Band418
ISSN (Print)0170-8643

Konferenz

KonferenzWorkshop on Identification in Automotive
LandÖsterreich
OrtLinz
Zeitraum15.07.201016.07.2010

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