Symbolic Regression Is Not Enough: It Takes a Village to Raise a Model

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

@InCollection{Kotanchek:2012:GPTP,

• author = "Mark E. Kotanchek and Ekaterina Vladislavleva and Guido Smits",
• title = "Symbolic Regression Is Not Enough: It Takes a Village to Raise a Model",
• booktitle = "Genetic Programming Theory and Practice X",
• year = "2012",
• series = "Genetic and Evolutionary Computation",
• editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore",
• publisher = "Springer",
• chapter = "13",
• pages = "187--203",
• address = "Ann Arbor, USA",
• month = "12-14 " # may,
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Post-processing, Model selection, Variable selection, Evolvability",
• isbn13 = "978-1-4614-6845-5",
• URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_13",
• DOI = "doi:10.1007/978-1-4614-6846-2_13",
• abstract = "From a real-world perspective, good enough has been achieved in the core representations and evolutionary strategies of genetic programming assuming state-of-the-art algorithms and implementations are being used. What is needed for industrial symbolic regression are tools to (a) explore and refine the data, (b) explore the developed model space and extract insight and guidance from the available sample of the infinite possibilities of model forms and (c) identify appropriate models for deployment as predictors, emulators, etc. This chapter focuses on the approaches used in DataModeler to address the modelling life cycle. A special focus in this chapter is the identification of driving variables and meta variables. Exploiting the diversity of search paths followed during independent evolutions and, then, looking at the distributions of variables and metavariable usage also provides an opportunity to gather key insights. The goal in this framework, however, is not to replace the modeller but, rather, to augment the inclusion of context and collection of insight by removing mechanistic requirements and facilitating the ability to think. We believe that the net result is higher quality and more robust models.",
• notes = "part of \cite{Riolo:2012:GPTP} published after the workshop in 2013",

}

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

Citations