Proposal and Preliminary Investigation of a Fitness Function for Partial Differential Models

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Peretta:2015:EuroGP,

• author = "Igor S. Peretta and Keiji Yamanaka and Paul Bourgine and Pierre Collet",
• title = "Proposal and Preliminary Investigation of a Fitness Function for Partial Differential Models",
• booktitle = "18th European Conference on Genetic Programming",
• year = "2015",
• editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
• series = "LNCS",
• volume = "9025",
• publisher = "Springer",
• pages = "179--191",
• address = "Copenhagen",
• month = "8-10 " # apr,
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, System modelling, Partial differential equations, Fitness function, Galerkin's method, Jacobi-Legendre polynomials, Tree-based Genetic Programming: Poster",
• isbn13 = "978-3-319-16500-4",
• DOI = "doi:10.1007/978-3-319-16501-1_15",
• abstract = "This work proposes and presents a preliminary investigation of a fitness evaluation scheme supported by a proper genotype representation intended to guide an under development expansion to EASEA/EASEA-CLOUD platforms to evolve partial differential equations as models for a specific system of interest, starting with measures from that system. A simple proof of concept using a dynamic bidirectional surface wave is presented, showing that the proposed fitness evaluation scheme is very promising to enable automate system modelling, even when dealing with up to +-10percent noise-added data.",
• notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015",

}

Genetic Programming entries for Igor Santos Peretta Keiji Yamanaka Paul Bourgine Pierre Collet

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