Recent Publications

Getting the word out about what challenges can be addressed with the DataModeler platform.

 

2020

 

2019

View More

  • Ekaterina Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagner – Predicting the energy output of wind farms based on weather data: Important variables and their correlation. Renewable Energy Journal, 2013, vol 50, p.236-243
  • N. Staelens, D. Deschrijver, E. Vladislavleva, B. Vermeulen, T. Dhaene and P. Demeester – Constructing a No-Reference H.264/AVC Bitstream-based Video Quality Metric using Genetic Programming-based Symbolic Regression. To appear in IEEE Transactions on Circuits and Systems for Video Technology Journal (2013)
  • Oliver Flasch, Martina Friese, Katya Vladislavleva, Thomas Bartz-Beielstein, Olaf Mersmann, Boris Naujoks, Joerg Stork, Martin Zaefferer – Comparing Ensemble-based Forecasting Methods for Smart-Metering Data. To appear in Proceedings of the Conference on Applications of Evolutionary Computation (Springer 2013)
  • Rick R. Riolo, Ekaterina Vladislavleva, Marylyn D. Ritchie, Jason H. Moore (Editors) – Genetic Programming Theory and Practice X. Series: Genetic and Evolutionary Computation, 2013, ISBN 978-1-4614-6845-5, Hardcover. Springer. May 2013
  • Mark E. Kotanchek, Ekaterina Vladislavleva, and Guido F. Smits – Symbolic Regression in Not Enough: It takes a village to raise a model invited chapter for the Tenth anniversary GPTP workshop, May 2012, Ann Arbor, MI, U.S.A. To appear in Genetic Programming in Theory and Practice X book
  • Kalyan Veeramachaneni, Ekaterina Vladislavleva, Una-May O’Reilly – Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization Journal on Genetic Programming and Evolvable Machines, March 2012, Volume 13, Issue 1, p.103-133
  • Martina Friese, Thomas Bartz-Beielstein, Katya Vladislavleva, Oliver Flasch, Olaf Mersmann, Boris Naujoks, Martin Zaefferer, Jörg Stork – Ensemble-based Model Selection for Smart-Metering Data. In Proceedings 22. Workshop Computational Intelligence, 2012, Germany, p. 215–228
  • Rick Riolo, Ekaterina Vladislavleva, Jason H. Moore (Editors) – Genetic Programming Theory and Practice IX. Series: Genetic and Evolutionary Computation, 2011, ISBN 978-1-4614-1769-9, Hardcover. Springer.
  • Sean Stijven, Wouter Minnebo, Katya Vladislavleva. – Separating the wheat from the chaff: on feature selection and feature importance in regression random forests and symbolic regression. In Steven Gustafson and Ekaterina Vladislavleva editors, 3rd symbolic regression and modeling workshop for GECCO 2011, pages 623-630, Dublin, Ireland, 2011. ACM.
  • Smits G.F., Vladislavleva E., Kotanchek M. – Scalable Symbolic Regression by Continuous Evolution with Very Small Populations. In Genetic Programming Theory and Practice VIII, Genetic and Evolutionary Computation Vol.8, Rick Riolo,Trent McConaghy, and Ekaterina Vladislavleva (Editors), Chapter 9, Published: 2011, Springer, ISBN: 978-1-4419-7746-5
  • Kalyan K. Veeramachaneni, Ekaterina Vladislavleva and Una-May O’Reilly – Feature extraction from optimization samples via ensemble based symbolic regression. In Annals of Mathematics and Artificial Intelligence Journal, 2011, Springer Netherlands, ISSN: 1012-2443
  • Riolo, Rick; McConaghy, Trent; Vladislavleva, Ekaterina (Editors) – Genetic Programming Theory and Practice VIII , Springer Series: Genetic and Evolutionary Computation, Vol. 8, 2011, ISBN 978-1-4419-7746-5, Hardcover
  • M. Kotanchek – Real World Data Modeling. In Juergen Branke et al. (Editors), GECCO’2010: Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages 2863-2896, Portland, Oregon, USA, 2010, ISBN: 978-1-4503-0073-5 Download
  • E. Vladislavleva, G. Smits, D. den Hertog. – On the Importance of Data Balancing for Symbolic Regression. In IEEE Transactions On Evolutionary Computation, Volume 14, Issue: 2, Pages: 252-277, Published: 2010, ISSN: 1089-778X, DOI: 10.1109/TEVC.2009.2029697.
  • Kotanchek M.E., Vladislavleva E.Y., Smits G.S. – Symbolic Regression as a Discovery Engine: Insights on Outliers and Prototypes. In Genetic Programming in Theory and Practice VII, Riolo Rick, O’Reilly Una-May and McConaghy Trent (Editors), Chapter 4, Published: 2010, ISBN:Â 978-1-4419-1625-9
  • Katya Vladislavleva, Kalyan Veeramachaneni, Matt Burland, Jason Parcon, Una-May O’Reilly, – Knowledge mining with genetic programming methods for variable selection in flavor design. In Juergen Branke et al. (Editors), GECCO’2010: Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages 941-948, Portland, Oregon, USA, 2010, ISBN13:978-1-4503-0072-8
  • Kalyan Veeramachaneni, Katya Vladislavleva, Matt Burland, Jason Parcon, Una-May O’Reilly. – Evolutionary Optimization of Flavors. In Juergen Branke et al. (Editors), GECCO’2010: Proceedings of the 12th annual conference on Genetic and evolutionary computation, pages 1291-1298, Portland, Oregon, USA, 2010, ISBN13: 978-1-4503-0072-8
  • Katya Vladislavleva, Kalyan Veeramachaneni, Una-May O’Reilly – Learning a Lot from Only a Little: Genetic Programming for PanelSegmentation on Sparce Sensory Evaluation Data. In A.I.Esparcia-Alcazar et al. (Editors), Proceedings of the 13th European Conference on Genetic Programming, EuroGP2010, Lecture Notes on Computer Science, Volume 6021, pages 244-255, Istanbul, 2010, Springer, ISBN13: 978-3-642-12147-0
  • Kalyan Veeramachaneni, Katya Vladislavleva, Una-May O’Reilly – Feature Extraction from Optimization Data via DataModeler’s Ensemble Symbolic Regression, LION, Lecture Notes in Computer Science, Vol. 6073, pp. 251-265, Springer, 2010
  • Mark Kotanchek and Guido Smits and Ekaterina Vladislavleva. – Exploiting Trustable Models via Pareto GP for Targeted Data Collection. In Rick L. Riolo, Terence Soule and Bill Worzel (Editors), Genetic Programming Theory and Practice VI, chapter 10, Published 2009, ISBN: 978-0-387-87622-1
  • E. Vladislavleva, G. Smits, D. den Hertog – Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming. In IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, Volume 13, Issue: 2, Pages: 333-349, Published: 2009, ISSN: 1089-778X, DOI:10.1109/TEVC.2008.926486.
  • Ekaterina Vladislavleva, Guido Smits and Mark Kotanchek – Soft Evolution of Robust Regression Models. In Rick L. Riolo, Terence Soule and Bill Worzel (Editors), Genetic Programming Theory and Practice V, chapter 2, Published 2008, ISBN: 978-0-387-76307-1
  • Mark Kotanchek, Guido Smits and Ekaterina Vladislavleva – Trustable Symbolic Regression Models — Using Ensembles, Interval Arithmetic and Pareto Fronts to develop Robust and Trust-aware Models. In Rick L. Riolo, Terence Soule and Bill Worzel (Editors), Genetic Programming Theory and Practice V, chapter 12, Published 2008, ISBN: 978-0-387-76307-1
  • Ekaterina Vladislavleva – Model-based Problem Solving through Symbolic Regression via Pareto Genetic Programming. PhD thesis, 264 pages, Tilburg University, Published 2008, ISBN13: 978 90 5668 217 0
  • A. Kordon, G. Smits and M.Kotanchek – Industrial evolutionary computing. In GECCO’2007 Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, Pages: 3297-3322, ACM, ISBN: 978-1-59593-698-1
  • Mark Kotanchek, Guido Smts and Ekaterina Vladislavleva. – Pursuing the Pareto Paradigm Tournaments, Algorithm Variations and Ordinal Optimization. In Rick L. Riolo, Terence Soule and Bill Worzel (Editors), Genetic Programming Theory and Practice IV, chapter 12, Published 2008, ISBN: 978-0-387-33375-5
  • Guido Smits, Ekaterina Vladislavleva – Ordinal Pareto Genetic Programming. In Gary G. Yen et al. (Editors), Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pages 3114-3120, Vancouver, BC, Canada, IEEE Press, Published 2006, ISBN: 0-7803-9487-9
  • Guido Smits, Arthur Kordon, Katherine Vladislavleva, Elsa Jordaan and Mark Kotanchek. – Variable Selection in Industrial Datasets using Pareto Genetic Programming. In Tina Yu, Rick Riolo and Bill Worzel (Editors), Genetic Programming Theory and Practice III, chapter 6, Published 2006, ISBN: 978-0-387-28110-0
  • Arthur Kordon, Flor Castillo, Guido Smits and Mark Kotanchek – Application Issues of Genetic Programming in Industry. In Tina Yu, Rick Riolo and Bill Worzel (Editors), Genetic Programming Theory and Practice III, chapter 16, Published 2006, ISBN: 978-0-387-28110-0
  • Guido Smits and Mark Kotanchek – Pareto-Front Exploitation in Symbolic Regression. In Una-May O’Reilly, Tina Yu, Rick L. Riolo, Bill Worzel (Editors), Genetic Programming Theory and Practice II, chapter 17, Published 2005, Springer, ISBN: 978-0-387-23253-9
  • Mark Kotanchek, Guido Smits and Arthur Kordon – Industrial Strength Genetic Programming. In Rick L. Riolo and Bill Worzel (Editors), Genetic Programming Theory and Practice, chapter 15, Published 2004, ISBN: 978-1-4020-7581-0
  • M. Kotanchek, A. Kordon, G. Smits, F. Castillo, R. Pell, M. B. Seasholtz, L. Chiang, P. Margl, P. K. Mercure, A. Kalos. – Evolutionary Computing in Dow Chemical. In Lawrence “Dave” Davis and Rajkumar Roy (Editors), GECCO-2002 Presentations in the Evolutionary Computation in Industry Track, pages 101-110, New York, New York, 2002.
  • A. Kordon, Hoang Pham, C. Bosnyak, M. Kotanchek,G. Smits. – Accelerating Industrial Fundamental Model Building with Symbolic Regression: A Case Study with Structure-Property Relationships. In Lawrence “Dave” Davis and Rajkumar Roy (Editors), GECCO-2002 Presentations in the Evolutionary Computation in Industry Track, pages 111-116, New York, New York, 2002.

Textbooks

Evolved Analytics LLC

PO Box 676225
Rancho Santa Fe, CA 92067
USA
+1-989-785-0513
info@evolved-analytics.com

Contact Us