Abstract
This contribution describes how symbolic regression can be used for knowledge discovery with the open-source software HeuristicLab. HeuristicLab includes a large set of algorithms and problems for combinatorial optimization and for regression and classification, including symbolic regression with genetic programming. It provides a rich GUI to analyze and compare algorithms and identified models. This contribution mainly focuses on specific aspects of symbolic regression that are unique to HeuristicLab, in particular, the identification of relevant variables and model simplification. © 2012 Springer-Verlag.
Cite
CITATION STYLE
Kronberger, G., Wagner, S., Kommenda, M., Beham, A., Scheibenpflug, A., & Affenzeller, M. (2012). Knowledge discovery through symbolic regression with HeuristicLab. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7524 LNAI, pp. 824–827). https://doi.org/10.1007/978-3-642-33486-3_56
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