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Symbolic Regression via Genetic Programming
Author

Mark Kotanchek
Organization: Evolved Analytics, LLC
Conference

2004 Wolfram Technology Conference
Conference location

Champaign IL
Description

Symbolic regression via genetic programming is a branch of empirical modeling that evolves summary expressions for available data. Although intrinsically difficult (the search space is infinite), recent algorithmic advances coupled with faster computers have enabled application of symbolic regression to a wide variety of industrial data sets. Unique benefits of symbolic regression include human insight and interpretability of model results, identification of key variables and variable combinations, and the generation of computationally simple models for deployment into operational models. In this presentation, we review the symbolic regression evolution process, practical issues, and approaches to managing, reviewing, and refining modeling results. A Mathematica symbolic regression package implementation will be demonstrated that stresses quality model development and a user-centric approach for model development, assessment, exploitation, and management.
Subject

*Mathematics > Discrete Mathematics > Cellular Automata
URL

http://www.wolfram.com/news/events/techconf2004
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markkotanchek.nb (365.8 KB) - Abstract of talk