Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data

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

@InCollection{Castillo:2006:GPTP,

• author = "Flor Castillo and Arthur Kordon and Guido Smits",
• title = "Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data",
• booktitle = "Genetic Programming Theory and Practice {IV}",
• year = "2006",
• editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
• volume = "5",
• series = "Genetic and Evolutionary Computation",
• pages = "149--166",
• address = "Ann Arbor",
• month = "11-13 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, symbolic regression, industrial applications, design of experiments, parameter selection",
• ISBN = "0-387-33375-4",
• DOI = "doi:10.1007/978-0-387-49650-4_10",
• size = "18 pages",
• abstract = "Symbolic regression based on Pareto front GP is a very effective approach for generating high-performance parsimonious empirical models acceptable for industrial applications. The chapter addresses the issue of finding the optimal parameter settings of Pareto front GP which direct the simulated evolution toward simple models with acceptable prediction error. A generic methodology based on statistical design of experiments is proposed. It includes determination of the number of replicates by half-width confidence intervals, determination of the significant factors by fractional factorial design of experiments, approaching the optimum by steepest ascent/descent, and local exploration around the optimum by Box Behnken design of experiments. The results from implementing the proposed methodology to different types of industrial data sets show that the statistically significant factors are the number of cascades, the number of generations, and the population size. The optimal values for the three parameters have been defined based on second order regression models with R2 higher than 0.97 for small, medium, and large-sized data sets. The robustness of the optimal parameters toward the types of data sets was explored and a robust setting for the three significant parameters was obtained. It reduces the calculation time by 30per cent to 50per cent without statistically significant reduction in the mean response.",
• notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007 after the workshop",

}

Genetic Programming entries for Flor A Castillo Arthur K Kordon Guido F Smits

Citations