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Elite Based Multiobjective Genetic Programming in Nonlinear Systems IdentificationPATELLI, A. , FERARIU, L.
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evolutionary algorithms, genetic programming, multiobjective optimization, nonlinear system identification
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
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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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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