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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Elite Based Multiobjective Genetic Programming in Nonlinear Systems Identification

PATELLI, A. See more information about PATELLI, A. on SCOPUS See more information about PATELLI, A. on IEEExplore See more information about PATELLI, A. on Web of Science, FERARIU, L. See more information about FERARIU, L. on SCOPUS See more information about FERARIU, L. on SCOPUS See more information about FERARIU, L. on Web of Science
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Download PDF pdficon (427 KB) | Citation | Downloads: 1,035 | Views: 4,428

Author keywords
evolutionary algorithms, genetic programming, multiobjective optimization, nonlinear system identification

References keywords
programming(7), genetic(6), evolutionary(6), systems(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-02-27
Volume 10, Issue 1, Year 2010, On page(s): 94 - 99
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01017
Web of Science Accession Number: 000275458900017
SCOPUS ID: 77954683610

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The nonlinear systems identification method described in the paper is based on genetic programming, a robust tool, able to ensure the simultaneous selection of model structure and parameters. The assessment of potential solutions is done via a multiobjective approach, making use of both accuracy and parsimony criteria, in order to encourage the selection of accurate and compact models, characterized by expected good generalization capabilities. The evolutionary process is implemented from an elitist standpoint, and upgraded by means of two original contributions, namely an adaptive niching mechanism and an elite clustering procedure. The authors have also suggested a set of enhancements to aid the genetic operators in effectively exploring the space of possible model structures. In symbiosis with the customized genetic operators, a QR local optimization procedure was integrated within the algorithm. It exploits the nonlinear, linear in parameter form that the working models are generated in, for providing a faster parameter computation. The performances of the proposed methodology were revealed on two applications, of different complexity levels: the identification of a simulated nonlinear system and the identification of an industrial plant.

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Cited-By CrossRef

[1] Parse-matrix evolution for symbolic regression, Luo, Changtong, Zhang, Shao-Liang, Engineering Applications of Artificial Intelligence, ISSN 0952-1976, Issue 6, Volume 25, 2012.
Digital Object Identifier: 10.1016/j.engappai.2012.05.015

[2] FPGA-Based Embedded System Architecture for Micro-Genetic Algorithms Applied to Parameters Optimization in Motion Control, JAEN-CUELLAR, A. Y., MORALES-VELAZQUEZ, L., ROMERO-TRONCOSO, R., OSORNIO-RIOS, R. A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 15, 2015.
Digital Object Identifier: 10.4316/AECE.2015.01004
[CrossRef] [Full text]

[3] Identification and prediction using symbolic regression alpha-beta, Torres-Treviño, L. M., Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, ISBN 9781450328814, 2014.
Digital Object Identifier: 10.1145/2598394.2609859

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