logo_somaSystematical Optimization of Models in Automation and IT

This BMBF-sponsored project was conducted in 2009-2013. Project partner were Leiden University, NL, University Bochum, Nurogames GmbH, Cologne and  divis GmbH, Dortmund.  

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Systematic optimization of models for complex applications in IT and automation, here with the goal of forecasting and optimal control of plants or processes, is the topic of this project. It poses still a great challenge for the practioner in computer science or engineering. In many cases it is not alone a problem of the right model parametrization, but also a task of intelligent data preprocessing and data selection. Here the project SOMA aims at developing and offering new solutions and tools.
 
Under the umbrella of SOMA several sub-projects were undertaken:  Tuned data mining (TDMR), gesture recognition, reinforcement learning for strategic games, intelligent methods for feature generation like slow feature analysis (SFA) or n-tuple systems. 
 
Topics: Applied computer science, modeling, simulation, learning system, computational intelligence (evolutionary algorithms, neural networks), data mining.
 
 

Publications SOMA

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1. Konen, Wolfgang: General Board Game Playing for Education and Research in Generic AI Game Learning. In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Hrsg.): IEEE Conference on Games, London, 2019. (Typ: Inproceedings | Links | BibTeX)
2. Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas: Solving Optimization Problems with High Conditioning by Means of Online Whitening. In: Lopez-Ibanez, Manuel (Hrsg.): Genetic and Evolutionary Computation Conference 2019 (GECCO'19), Prague, S. 243-244, ACM, 2019. (Typ: Inproceedings | Links | BibTeX)
3. Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas: SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning. arXiv preprint arXiv:1904.08397 2019. (Typ: Forschungsbericht | Links | BibTeX)
4. Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas: How to Solve the Dilemma of Margin-Based Equality Constraint Handling Methods. In: at-Automatisierungstechnik, submitted , 2019. (Typ: Artikel | BibTeX)
5. Buhl, Henning; Konen, Wolfgang; Thill, Markus: Deep Learning mit Keras und Tensorflow. Vortrag auf DEBRL2019 (Digital Exchange Bergisches Rheinland 2019), 2019. (Typ: Sonstige | Links | BibTeX)
6. Konen, Wolfgang: The GBG Class Interface Tutorial V2.0: General Board Game Playing and Learning. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2019. (Typ: Forschungsbericht | Links | BibTeX)
7. Barsnick, Felix: Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen. TH Köln -- University of Applied Sciences, 2019, (Master thesis). (Typ: Abschlussarbeit | Links | BibTeX)
8. Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas: How to Solve the Dilemma of Margin-Based Equality Handling Methods. In: Hoffmann, Frank; Hüllermeier, Eyke; Mikut, Ralf (Hrsg.): Proceedings - 28. Workshop Computational Intelligence, Dortmund, 29. - 30. November 2018, S. 257-270, KIT Scientific Publishing, Karlsruhe, 2018, ISBN: 978-3-7315-0845-8, (Young Author Award). (Typ: Inproceedings | Links | BibTeX)
9. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Online Adaptable Time Series Anomaly Detection with Discrete Wavelet Transforms and Multivariate Gaussian Distributions. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2018, (submitted to Archives of Data Sciences, Series A (ECDA'2018), preprint available at http://www.gm.fh-koeln.de/ciopwebpub/Thill18a.d/AoDS2018.pdf). (Typ: Forschungsbericht | Links | BibTeX)
10. Konen, Wolfgang; Koch, Patrick: The TDMR 2.0 Package: Tuned Data Mining in R. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2018), 2018, (Last update: April 2018 (original version: 2012)). (Typ: Forschungsbericht | Links | BibTeX)
11. Konen, Wolfgang; Koch, Patrick: The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2018), 2018, (Last update: April 2018 (original version: 2012)). (Typ: Forschungsbericht | Links | BibTeX)
12. Bagheri, Samineh; Konen, Wolfgang; Bäck, Thomas: Comparing Kriging and Radial Basis Function Surrogates. In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 243-259, Universitätsverlag Karlsruhe, 2017. (Typ: Inproceedings | Links | BibTeX)
13. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Anomaly Detection in Time Series with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 27. Workshop Computational Intelligence, S. 67-71, Universitätsverlag Karlsruhe, 2017. (Typ: Inproceedings | Links | BibTeX)
14. Bagheri, Samineh; Konen, Wolfgang; Allmendinger, Richard; Branke, Jürgen; Deb, Kalyanmoy; Fieldsend, Jonathan; Quagliarella, Domenico; Sindhya, Karthik: Constraint Handling in Efficient Global Optimization. In: Bosman, Peter A N (Hrsg.): Genetic and Evolutionary Computation Conference 2017 (GECCO'17), Berlin, S. 1, ACM, 2017. (Typ: Inproceedings | Links | BibTeX)
15. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation. In: Valenzuela, Olga; Rojas, Ignacio; others, (Hrsg.): International Work-Conference on Time Series (ITISE2017), 2017. (Typ: Inproceedings | Links | BibTeX)
16. Konen, Wolfgang: The GBG Class Interface Tutorial: General Board Game Playing and Learning. Research Center CIOP (Computational Intelligence, Optimization and Data Mining) TH Köln - University of Applied Science, 2017, (e-print published at http://www.gm.fh-koeln.de/ciopwebpub/Kone17a.d/TR-GBG.pdf). (Typ: Forschungsbericht | Links | BibTeX)
17. Bagheri, Samineh; Konen, Wolfgang; Emmerich, Michael; Bäck, Thomas: Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets. In: Applied Soft Computing, 61 , S. 377–393, 2017, ISSN: 1568-4946. (Typ: Artikel | Links | BibTeX)
18. Galitzki, Kevin: Selbstlernende Agenten für das skalierbare Spiel Hex: Untersuchung verschiedener KI-Verfahren im GBG-Framework. TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis). (Typ: Abschlussarbeit | Links | BibTeX)
19. Kutsch, Johannes: KI-Agenten fur das Spiel 2048: Untersuchung von Lernalgorithmen für nichtdeterministische Spiele. TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis). (Typ: Abschlussarbeit | Links | BibTeX)
20. Thill, Markus; Konen, Wolfgang; Bäck, Thomas: Online anomaly detection on the Webscope S5 dataset: A comparative study. In: IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2017), S. 1, Springer 2017. (Typ: Inproceedings | Links | BibTeX)
230 Einträge « 1 von 12 »

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2018

Konen, Wolfgang; Koch, Patrick

The TDMR 2.0 Package: Tuned Data Mining in R Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2018), 2018, (Last update: April 2018 (original version: 2012)).

Links | BibTeX

Konen, Wolfgang; Koch, Patrick

The TDMR 2.0 Tutorial: Examples for Tuned Data Mining in R Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2018), 2018, (Last update: April 2018 (original version: 2012)).

Links | BibTeX

2013

Koch, Patrick; Konen, Wolfgang

Subsampling strategies in SVM ensembles Inproceedings

Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 23. Workshop Computational Intelligence, S. 119–134, Universitätsverlag Karlsruhe, 2013.

Links | BibTeX

Stork, Jörg; Ramos, Ricardo; Koch, Patrick; Konen, Wolfgang

SVM ensembles are better when different kernel types are combined Inproceedings

Lausen, Berthold (Hrsg.): European Conference on Data Analysis (ECDA13), (under review), 2013.

Links | BibTeX

2012

Konen, Wolfgang; Koch, Patrick

The TDMR Package: Tuned Data Mining in R Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2012), 2012, (Last update: June 2017).

Links | BibTeX

Konen, Wolfgang; Koch, Patrick

The TDMR Tutorial: Examples for Tuned Data Mining in R Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (03/2012), 2012, (Last update: May, 2016).

Links | BibTeX

Konen, Wolfgang; Koch, Patrick

The TDMR Framework: Tuned Data Mining in R Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (02/2012), 2012.

Links | BibTeX

Koch, Patrick; Konen, Wolfgang

Efficient sampling and handling of variance in tuning data mining models Inproceedings

Coello, Carlos Coello A; Cutello, Vincenzo; others, (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 195–205, Springer, Heidelberg, 2012.

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Thill, Markus; Koch, Patrick; Konen, Wolfgang

Reinforcement learning with n-tuples on the game Connect-4 Inproceedings

Coello, Carlos Coello A; Cutello, Vincenzo (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 184–194, Springer, Heidelberg, 2012.

Links | BibTeX

Koch, Patrick; Bischl, Bernd; Flasch, Oliver; Bartz-Beielstein, Thomas; Weihs, Claus; Konen, Wolfgang

Tuning and Evolution of Support Vector Kernels Artikel

Evolutionary Intelligence, 5 , S. 153–170, 2012.

Links | BibTeX

2011

Koch, Patrick; Konen, Wolfgang; Naujoks, Boris; Flasch, Oliver; Friese, Martina; Zaefferer, Martin; Bartz-Beielstein, Thomas

Tuned Data Mining in R Inproceedings

Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 21. Workshop Computational Intelligence, S. 147–160, Universitätsverlag Karlsruhe, 2011.

BibTeX

Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas; Friese, Martina; Naujoks, Boris

Tuned Data Mining: A Benchmark Study on Different Tuners Inproceedings

Krasnogor, Natalio (Hrsg.): GECCO '11: Proceedings of the 13th Annual Conference on Genetic andEvolutionary Computation, S. 1995–2002, 2011.

BibTeX

Konen, Wolfgang

Der SFA-Algorithmus für Klassifikation Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (08/11), 2011, ISSN: 2191-365X.

Links | BibTeX

Koch, Patrick; Bischl, Bernd; Flasch, Oliver; Bartz-Beielstein, Thomas; Konen, Wolfgang

On the Tuning and Evolution of Support Vector Kernels Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Scienceand Engineering Science, (04/11), 2011, ISSN: 2191-365X.

Links | BibTeX

Konen, Wolfgang; Koch, Patrick

The slowness principle: SFA can detect different slow components in nonstationary time series Artikel

International Journal of Innovative Computing and Applications (IJICA), 3 (1), S. 3–10, 2011.

Links | BibTeX

Konen, Wolfgang

SFA classification with few training data: Improvements with parametric bootstrap Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, (09/11), 2011, ISSN: 2191-365X.

Links | BibTeX

2010

Konen, Wolfgang; Koch, Patrick; Flasch, Oliver; Bartz-Beielstein, Thomas

Parameter-Tuned Data Mining: A General Framework Inproceedings

Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, Universitätsverlag Karlsruhe, 2010.

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Hein, Kristine

Gestenerkennung mit Slow Feature Analysis (SFA) - Klassifizierung von beschleunigungsbasierten 3Đ-Gesten des Wii-Controllers Forschungsbericht

FH Köln 2010.

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Koch, Patrick; Konen, Wolfgang; Flasch, Oliver; Bartz-Beielstein, Thomas

Optimizing Support Vector Machines for Stormwater Prediction Inproceedings

Bartz-Beielstein, Thomas; Chiarandini, ; Paquete, ; Preuss, Mike (Hrsg.): Proceedings of Workshop on Experimental Methods for the Assessment of Computational Systems joint to PPSN2010, S. 47–59, TU Dortmund, 2010.

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Koch, Patrick; Konen, Wolfgang; Flasch, Oliver; Bartz-Beielstein, Thomas

Optimization of Support Vector Regression Models for Stormwater Prediction Inproceedings

Hoffmann, Frank; Hüllermeier, Eyke (Hrsg.): Proceedings 20. Workshop Computational Intelligence, S. 146–160, Universitätsverlag Karlsruhe, 2010.

BibTeX

Koch, Patrick; Konen, Wolfgang; Hein, Kristine

Gesture Recognition on Few Training Data using Slow Feature Analysis and Parametric Bootstrap Inproceedings

2010 International Joint Conference on Neural Networks, 2010.

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Konen, Wolfgang; Koch, Patrick

How slow is slow? SFA detects signals that are slower than the driving force Inproceedings

Filipic, B; Silc, J (Hrsg.): Proc. 4th Int. Conf. on Bioinspired Optimization Methods and their Applications, BIOMA, Ljubljana, Slovenia, 2010.

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2009

Konen, Wolfgang

How slow is slow? SFA detects signals that are slower than the driving force Forschungsbericht

Cologne University of Applied Sciences (05/09), 2009, (e-print published at http://arxiv.org/abs/0911.4397).

Links | BibTeX

Konen, Wolfgang

On the numeric stability of the SFA implementation sfa-tk Forschungsbericht

Cologne University of Applied Sciences (05/10), 2009, (e-print published at http://arxiv.org/abs/0912.1064).

Links | BibTeX