Meta-modeling by symbolic regression and Pareto simulated annealing

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

@TechReport{oai:wo.uvt.nl:193400,

• title = "Meta-modeling by symbolic regression and Pareto simulated annealing",
• author = "Erwin Stinstra and Gijs Rennen and Geert Teeuwen",
• year = "2006",
• institution = "Tilburg University",
• type = "Internal report",
• number = "No. 2006-15",
• address = "Holland",
• month = mar,
• bibsource = "OAI-PMH server at arno.uvt.nl",
• oai = "oai:wo.uvt.nl:193400",
• rights = "(c) Universiteit van Tilburg",
• keywords = "genetic algorithms, genetic programming, approximation, meta-modeling, Pareto simulated annealing, symbolic regression",
• URL = "http://greywww.kub.nl:2080/greyfiles/center/2006/doc/15.pdf",
• URL = "http://dbiref.uvt.nl/iPort?request=full_record\&db=wo\&language=eng\&query=193400",
• abstract = "The subject of this paper is a new approach to Symbolic Regression. Other publications on Symbolic Regression use Genetic Programming. This paper describes an alternative method based on Pareto Simulated Annealing. Our method is based on linear regression for the estimation of constants. Interval arithmetic is applied to ensure the consistency of a model. In order to prevent over-fitting, we merit a model not only on predictions in the data points, but also on the complexity of a model. For the complexity we introduce a new measure. We compare our new method with the Kriging meta-model and against a Symbolic Regression meta-model based on Genetic Programming. We conclude that Pareto Simulated Annealing based Symbolic Regression is very competitive compared to the other meta-model approaches",

}

Genetic Programming entries for Erwin Stinstra Gijs Rennen Geert Teeuwen

Citations