Industrial Data Modeling with DataModeler: Pareto-Aware Symbolic Regression. Evolved Analytics’ DataModeler package (www.evolved-analytics.com) for Mathematica was developed over many years of active industrial modeling. The techniques it embodies have been applied in production trouble-shooting, bioreactor control, financial prediction, emissions monitoring, and elsewhere. We point out the growing need for industrial-strength modeling, review key strengths of genetic programming, demonstrate the capabilities of the DataModeler package, and demonstrate the future impact of exploratory modeling using case studies and real-world industrial data. We conclude with a discussion of plans for DataModeler 2.0.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Cozad, Alison; Sahinidis, Nikolaos V.: A global MINLP approach to symbolic regression (2018)
- Parezanović, Vladimir; Cordier, Laurent; Spohn, Andreas; Duriez, Thomas; Noack, Bernd R.; Bonnet, Jean-Paul; Segond, Marc; Abel, Markus; Brunton, Steven L.: Frequency selection by feedback control in a turbulent shear flow (2016)
- Nguyen, Anh Quang; Sutton, Andrew M.; Neumann, Frank: Population size matters: rigorous runtime results for maximizing the hypervolume indicator (2015)
- Paláncz, B.; Awange, J. L.; Völgyesi, L.: Correction of gravimetric geoid using symbolic regression (2015)
- Gao, Liang; Xiao, Mi; Shao, Xinyu; Jiang, Ping; Nie, Li; Qiu, Haobo: Analysis of gene expression programming for approximation in engineering design (2012) ioport
- Veeramachaneni, Kalyan K.; Vladislavleva, Ekaterina; O’Reilly, Una-May: Feature extraction from optimization samples via ensemble based symbolic regression (2011)
- Gorissen, Dirk; Dhaene, Tom; De Turck, Filip: Evolutionary model type selection for global surrogate modeling (2009)