Better Solutions Faster: Soft Evolution of Robust Regression Models In Pareto genetic programming

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

@InCollection{Vladislavleva:2007:GPTP,

• author = "Ekaterina Vladislavleva and Guido Smits and Mark Kotanchek",
• title = "Better Solutions Faster: Soft Evolution of Robust Regression Models In Pareto genetic programming",
• booktitle = "Genetic Programming Theory and Practice {V}",
• year = "2007",
• editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
• series = "Genetic and Evolutionary Computation",
• chapter = "2",
• pages = "13--32",
• address = "Ann Arbor",
• month = "17-19" # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-0-387-76308-8",
• DOI = "doi:10.1007/978-0-387-76308-8_2",
• size = "19 pages",
• abstract = "Better solutions faster is the reality of the industrial modelling world, now more than ever. Efficiency requirements, market pressures, and ever changing data force us to use symbolic regression via genetic programming (GP) in a highly automated fashion. This is why we want our GP system to produce simple solutions of the highest possible quality with the lowest computational effort, and a high consistency in the results of independent GP runs. In this chapter, we show that genetic programming with a focus on ranking in combination with goal softening is a very powerful way to improve the efficiency and effectiveness of the evolutionary search. Our strategy consists of partial fitness evaluations of individuals on random subsets of the original data set, with a gradual increase in the subset size in consecutive generations. From a series of experiments performed on three test problems, we observed that those evolutions that started from the smallest subset sizes (10percent) consistently led to results that are superior in terms of the goodness of fit, consistency between independent runs, and computational effort. Our experience indicates that solutions obtained using this approach are also less complex and more robust against over-fitting. We find that the near-optimal strategy of allocating computational budget over a GP run is to evenly distribute it over all generations. This implies that initially, more individuals can be evaluated using small subset sizes, promoting better exploration. Exploitation becomes more important towards the end of the run, when all individuals are evaluated using the full data set with correspondingly smaller population sizes.",
• notes = "part of \cite{Riolo:2007:GPTP} published 2008",
• affiliation = "Tilburg University Tilburg The Netherlands",

}

Genetic Programming entries for Ekaterina (Katya) Vladislavleva Guido F Smits Mark Kotanchek

Citations