Automated Discovery of Numerical Approximation Formulae Via Genetic Programming

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

@InProceedings{streeter:2001:gecco,

• title = "Automated Discovery of Numerical Approximation Formulae Via Genetic Programming",
• author = "Matthew Streeter and Lee A. Becker",
• pages = "147--154",
• year = "2001",
• publisher = "Morgan Kaufmann",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001)",
• editor = "Lee Spector and Erik D. Goodman and Annie Wu and W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and Sandip Sen and Marco Dorigo and Shahram Pezeshk and Max H. Garzon and Edmund Burke",
• address = "San Francisco, California, USA",
• publisher_address = "San Francisco, CA 94104, USA",
• month = "7-11 " # jul,
• keywords = "genetic algorithms, genetic programming, approximations, symbolic, regression, Pareto optimality",
• ISBN = "1-55860-774-9",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2001/d01.pdf",
• URL = "https://dl.acm.org/doi/10.5555/2955239.2955257",
• size = "8 pages",
• abstract = "This paper describes the use of genetic programming to automate the discovery of numerical approximation formulae. The authors present results involving rediscovery of known approximations for Harmonic numbers and discovery of rational polynomial approximations for functions of one or more variables, the latter of which are compared to Pade approximations obtained through a symbolic mathematics package. For functions of a single variable, it is shown that evolved solutions can be considered superior to Pade approximations, which represent a powerful technique from numerical analysis, given certain tradeoffs between approximation cost and accuracy, while for functions of more than one variable, we are able to evolve rational polynomial approximations where no Pade approximation can be computed. Further, it is shown that evolved approximations can be refined through the evolution of approximations to their error function. Based on these results, we consider genetic programming to be a powerful and effective technique for the automated discovery of numerical approximation formulae.",
• notes = "GECCO-2001 A joint meeting of the tenth International Conference on Genetic Algorithms (ICGA-2001) and the sixth Annual Genetic Programming Conference (GP-2001) Part of \cite{spector:2001:GECCO} See also streeter:2001:TBSW",

}

Genetic Programming entries for Matthew J Streeter Lee A Becker

Citations