A comparison of techniques to get sparse rational approximations for linear fractional representations

Created by W.Langdon from gp-bibliography.bib Revision:1.4782

@InProceedings{Roos:2014:ICAS,

• title = "A comparison of techniques to get sparse rational approximations for linear fractional representations",
• author = "Clement Roos and Georges Hardier and Carsten Doell",
• booktitle = "29th Congress of the International Council of the Aeronautical Sciences (ICAS 2014)",
• year = "2014",
• address = "Saint-Petersburg, Russia",
• month = "7-12 " # sep,
• organisation = "International Council of the Aeronautical Sciences (ICAS)",
• publisher = "HAL CCSD",
• keywords = "genetic algorithms, genetic programming, POLYNOMIAL AND RATIONAL APPROXIMATION, LINEAR FRACTIONAL REPRESENTATION, SURROGATE MODELS, EVOLUTIONARY ALGORITHMS",
• ISSN = "01088599",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Onera - The French Aerospace Lab (Toulouse) and ONERA",
• coverage = "Saint-Petersburg, Russia",
• description = "International audience",
• identifier = "hal-01088599",
• language = "en",
• oai = "oai:HAL:hal-01088599v1",
• URL = "https://hal.archives-ouvertes.fr/hal-01088599",
• URL = "https://hal.archives-ouvertes.fr/hal-01088599/document",
• size = "12 pages",
• abstract = "The objective of this paper is to stress that the size of a Linear Fractional Representation (LFR) significantly depends on the way tabulated or irrational data are approximated during the prior modelling process. It is notably shown that rational approximants can result in much smaller LFR than polynomial ones. Accordingly, 2 new methods are proposed to generate sparse rational models, which avoid data over fitting and lead to simple yet accurate LFR. The 1 st one builds a parsimonious modelling based on surrogate models and a new powerful global optimisation method, and then translates the result into a fractional form. The 2 nd one looks for a rational approximant in a single step thanks to a symbolic regression technique, and relies on Genetic Programming to select sparse monomials. This work takes place in a more general project led by ONERA/DCSD and aimed at developing a Systems Modelling, Analysis and Control Toolbox (SMAC) for Matlab.",
• notes = "Toulouse - Onera - The French Aerospace Lab http://www.icas2014.com/",

}

Genetic Programming entries for Clement Roos Georges Hardier Carsten Doell

Citations