Elite Based Multiobjective Genetic Programming in Nonlinear Systems Identification

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@Article{Patelli:2010:AECE,

• title = "Elite Based Multiobjective Genetic Programming in Nonlinear Systems Identification",
• author = "Alina Patelli and Lavinia Ferariu",
• journal = "Advances in Electrical and Computer Engineering",
• year = "2010",
• volume = "10",
• number = "1",
• pages = "94--99",
• month = feb,
• keywords = "genetic algorithms, genetic programming, evolutionary algorithms, multiobjective optimization, nonlinear system identification",
• ISSN = "1582-7445",
• URL = "http://www.aece.ro/abstractplus.php?year=2010&number=1&article=17",
• broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=15827445\&date=2010\&volume=10\&issue=1\&spage=94",
• DOI = "doi:10.4316/AECE.2010.01017",
• publisher = "Stefan cel Mare University of Suceava",
• bibsource = "OAI-PMH server at www.doaj.org",
• oai = "oai:doaj-articles:7de67791d79585b8b25988d832c27761",
• size = "6 pages",
• abstract = "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.",

}

Genetic Programming entries for Alina Patelli Lavinia Ferariu

Citations