Scientific Publications

[2017]

• Dirk De Vos, Abdiravuf Dzhurakhalov, Sean Stijven, Przemyslaw Klosiewicz, Gerrit Beemster, Jan Broeckhove, – Virtual plant tissue: building blocks for next-generation plant growth simulation. In Frontiers in plant science, 2017, volume 8, pp. 686.

• Elise Kuylen, Sean Stijven, Jan Broeckhove, Lander Willem, – Social contact patterns in an individual-based simulator for the transmission of infectious diseases (stride). In Procedia Computer Science, 2017, volume 108, pp. 2438-2442, Publisher: Elsevier.

[2016]

• Sean Stijven, Ekaterina Vladislavleva, Arthur Kordon, Lander Willem, Mark Kotanchek, – Prime-Time: Symbolic Regression Takes Its Place in the Real World. In Genetic Programming Theory and Practice XIII, 2016, pp. 241-260, ISBN 978-3-319-34221-4

[2015]

• Ekaterina Vladislavleva, Guido Smits, Mark Kotanchek – Computational Intelligence in Industrial Applications. In Springer Handbook of Computational Intelligence, 2015, Chapter 57, pp. 1143-1158, ISBN 978-3-662-43504-5

• Rein Houthooft, Joeri Ruyssinck, Joachim van der Herten, Sean Stijven, Ivo Couckuyt, and others – Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores. In Artificial intelligence in medicine, 2015, volume 63, pp. 191-207, Publisher: Elsevier

• Lander Willem, Sean Stijven, Niel Hens, Ekaterina Vladislavleva, Jan Broeckhove, Philippe Beutels, – Individual-based models: powerful or power struggle?. In Communications in agricultural and applied biological sciences, 2015, volume 80, pp. 97-102.

• Lander Willem, Sean Stijven, Engelbert Tijskens, Philippe Beutels, Niel Hens, Jan Broeckhove, – Optimizing agent-based transmission models for infectious diseases. In BMC bioinformatics, 2015, volume 15, pp. 183.

[2014]

• Lander Willem, Sean Stijven, Ekaterina Vladislavleva, Jan Broeckhove, Philippe Beutels, Niel Hens – Active learning to understand infectious disease models and improve policy making. In PLoS Computational Biology, 04/2014, DOI: 10.1371/journal.pcbi.1003563

• Sean Stijven, Ruben Van den Bossche, Ekaterina (Katya) Vladislavleva, Kurt Vanmechelen, Jan Broeckhove, Mark Kotanchek – Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models In book: Genetic Programming Theory and Practice XI, Publisher: Springer New York, 2014, pp.47-63

[2013]

• 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. (download pre-print) 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. To appear in May 2013

[2012]

• 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

[2011]

• 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

[2010]

• M. Kotanchek – Real World Data Modeling. In Juergen Branke etal. (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

[2009]

• 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

[2008]

• 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

[2007]

• 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

[2006]

• Guido Smits, Ekaterina Vladislavleva – Ordinal Pareto Genetic Programming. In Gary G. Yen etal. (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. – Variable Selection in Industrial Datasets using Pareto Genetic Programming. In Tina Yu, Rick Riolo and Bill Worzel (Editors), Genetic Programming Theory and Practice III (2005), chapter 6, Published 2006, ISBN: 978-0-387-28110-0

[2005]

• Vladislavleva E. J. – Symbolic Regression via Genetic Programming. Thesis for Dow Benelux NV, Eindhoven, the Netherlands, Technische Universiteit Eindhoven, 02/2005