Symbolic Regression via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{sbrn2000meta029,

• author = "Douglas A. Augusto and Helio J. C. Barbosa",
• title = "Symbolic Regression via Genetic Programming",
• booktitle = "{VI} Brazilian Symposium on Neural Networks (SBRN'00)",
• year = "2000",
• pages = "173",
• address = "Rio de Janeiro, RJ, Brazil",
• month = jan # " 22-25",
• publisher = "IEEE",
• note = "VI Simposio Brasileiro de Redes Neurais",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-7695-0856-1",
• identifier = "sbrn2000article029",
• language = "eng",
• source = "sbrn2000",
• broken = "http://csdl.computer.org/comp/proceedings/sbrn/2000/0856/00/08560173abs.htm",
• DOI = "doi:10.1109/SBRN.2000.889734",
• abstract = "In this work, we present an implementation of symbolic regression, which is based on genetic programming (GP). Unfortunately, standard implementations of GP in compiled languages are not usually the most efficient ones. The present approach employs a simple representation for tree-like structures by making use of Read's linear code, leading to more simplicity and better performance when compared with traditional GP implementations. Creation, crossover and mutation of individuals are formalized. An extension allowing for the creation of random coefficients is presented. The efficiency of the proposed implementation was confirmed in computational experiments, which are summarized in this paper.",

}

Genetic Programming entries for Douglas A Augusto Helio J C Barbosa

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