[TB] (Theoretical Biology,Theoretische Biologie) Ruhr-Universität Bochum [Ruhr-Universität Bochum]
[INI] [INI]

Research Group
Optimization of Adaptive Systems
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Chairs
• Prof. Dr. Laurenz Wiskott
• Prof. Dr. Gregor Schöner

Research groups
• Theory of embodied cognition
Prof. Dr. Gregor Schöner
• Theory of Neural Systems
Prof. Dr. Laurenz Wiskott
• Neural Plasticity Lab
PD Dr. Hubert Dinse
• Real-Time Optical Imaging Lab
Dr. Dirk Jancke
• Organic Computing
Dr. Rolf Würtz
• Optimization of adaptive systems
Jun.-Prof. Dr. Christian Igel
• Autonomous robotics
Dr. Ioannis Iossifidis
• Medical Image Processing
Dr. Susanne Winter
• Real-time computer vision
Jan Salmen
• Multi-sensory fusion
Dr. Andrey Bogdanov

Optimization of Adaptive Systems: Publications

[People] [Publications] [Current grant support] [Software]

Heidrich-Meisner, V. and C. Igel (2009). Neuroevolution strategies for episodic reinforcement learning. Journal of Algorithms. Accepted.
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Suttorp, T., N. Hansen, and C. Igel (2009). Efficient covariance matrix update for variable metric evolution strategies. Machine Learning. doi: 10.1007/s10994-009-5102-1.
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Glasmachers, T. and C. Igel (2008b). Uncertainty handling in model selection for support vector machines. In G. Rudolph (Ed.), Parallel Problem Solving from Nature (PPSN X), Volume 5199 of LNCS, pp. 185-194. Springer-Verlag.
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Heidrich-Meisner, V. and C. Igel (2008a). Evolution strategies for direct policy search. In G. Rudolph (Ed.), Parallel Problem Solving from Nature (PPSN X), Volume 5199 of LNCS, pp. 428-437. Springer-Verlag.
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Glasmachers, T. (2008). On related violating pairs for working set selection in smo algorithms. In M. Verleysen (Ed.), 16th European Symposium on Artificial Neural Networks (ESANN 2008), pp. 475-480. Evere, Belgien: d-side publications.
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Glasmachers, T. and C. Igel (2008a). Second order SMO improves SVM online and active learning. Neural Computation 20(2), 374-382.
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Heidrich-Meisner, V. and C. Igel (2008c). Similarities and differences between policy gradient methods and evolution strategies. In M. Verleysen (Ed.), 16th European Symposium on Artificial Neural Networks (ESANN 2008), pp. 149-154. Evere, Belgien: d-side publications.
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Igel, C. (2008). Efficient covariance matrix update for evolution strategies. In D. V. Arnold, A. Auger, J. Rowe, and C. Witt (Eds.), Theory of Evolutionary Algorithms, Number 08051 in Dagstuhl Seminar Proceedings, Abstract Collection. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl.
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Igel, C. and B. Sendhoff (2008). Genesis of organic computing systems: Coupling evolution and learning. In R. Würtz (Ed.), Organic Computing, Chapter 7, pp. 141-166. Springer-Verlag.
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Suttorp, T. and C. Igel (2008). Approximation of Gaussian process regression models after training. In M. Verleysen (Ed.), 16th European Symposium on Artificial Neural Networks (ESANN 2008), pp. 427-432. Evere, Belgien: d-side publications.
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Voß, T., N. Beume, G. Rudolph, and C. Igel (2008). Scalarization versus indicator-based selection in multi-objective CMA evolution strategies. In IEEE Congress on Evolutionary Computation 2008 (CEC 2008), pp. 3041-3048. IEEE Press.
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Winter, S., B. Brendel, I. Pechlivanis, K. Schmieder, and C. Igel (2008). Registration of CT and intraoperative 3D ultrasound images of the spine using evolutionary and gradient-based methods. IEEE Transactions on Evolutionary Computation 12(3), 284-296.
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Heidrich-Meisner, V. and C. Igel (2008e). Variable metric reinforcement learning methods applied to the noisy mountain car problem. In S. Girgin et al. (Eds.), European Workshop on Reinforcement Learning (EWRL 2008), Volume 5323 of LNAI, pp. 136-150. Springer-Verlag.
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Heidrich-Meisner, V. and C. Igel (2008d). Uncertainty handling in evolutionary direct policy search. In Y. Engel, M. Ghavamzadeh, P. Poupart, and S. Mannor (Eds.), NIPS-08 Workshop on Model Uncertainty and Risk in Reinforcement Learning.
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Heidrich-Meisner, V. and C. Igel (2008b). Learning behavioral policies using extrinsic perturbations on the level of synapses. Frontiers in Computational Neuroscience. Conference Abstract: Bernstein Symposium 2008.
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Heidrich-Meisner, V., M. Lauer, C. Igel, and M. Riedmiller (2007). Reinforcement learning in a nutshell. In M. Verleysen (Ed.), 15th European Symposium on Artificial Neural Networks (ESANN 2007), pp. 277-288. Evere, Belgien: d-side publications.
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Igel, C., T. Glasmachers, B. Mersch, N. Pfeifer, and P. Meinicke (2007). Gradient-based optimization of kernel-target alignment for sequence kernels applied to bacterial gene start detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(2), 216-226.
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Igel, C., N. Hansen, and S. Roth (2007). Covariance matrix adaptation for multi-objective optimization. Evolutionary Computation 15(1), 1-28.
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Igel, C., T. Suttorp, and N. Hansen (2007). Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES. In S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu, and T. Murata (Eds.), Fourth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2007), Volume 4403 of LNCS, pp. 171-185. Springer-Verlag.
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König, A., M. Köppen, A. Abraham, C. Igel, and N. Kasabov (Eds.) (2007). International Conference on Hybrid Intelligent Systems (HIS 2007). IEEE Computer Society.
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Liebenrodt, K., M. H. J. Busch, S. Mateiescu, C. Igel, S. Winter, and D. H. W. Grönemeyer (2007). Protonenresonanzspektroskopie des Gehirns mit kurzer Echozeit: Unterstützung der Gewebeklassifizierung durch künstliche neuronale Netzwerke. Biomedizinische Technik (BMT) 52 (suppl.).
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Mersch, B., T. Glasmachers, P. Meinicke, and C. Igel (2007). Evolutionary optimization of sequence kernels for detection of bacterial gene starts. International Journal of Neural Systems 17(5), 369-381. Special issue on selected papers presented at the International Conference on Artificial Neural Networks (ICANN 2006).
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Meyer, J., D. Jancke, and C. Igel (2007). Modelling cortical activity underlying apparent and real motion perception. In K.-A. Hossmann (Ed.), Symposium `Neuro-Visionen 4', pp. 257-258. Verlag Ferdinand Schöningh.
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Niehaus, J., C. Igel, and W. Banzhaf (2007). Reducing the number of fitness evaluations in graph genetic programming using a canonical graph indexed database. Evolutionary Computation 15(2), 199-221.
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Salmen, J., T. Suttorp, J. Edelbrunner, and C. Igel (2007). Evolutionary optimization of wavelet feature sets for real-time pedestrian classification. In A. König, M. Köppen, A. Abraham, C. Igel, and N. Kasabov (Eds.), International Conference on Hybrid Intelligent Systems (HIS 2007), pp. 222-227. IEEE Computer Society.
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Suttorp, T. and C. Igel (2007). Resilient simplification of kernel classifiers. In J. M. de Sá et al. (Eds.), International Conference on Artificial Neural Networks (ICANN 2007), Volume 4668 of LNCS, pp. 139-148. Springer-Verlag.
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Igel, C. (2006b). Computational efficient covariance matrix update and the multi-objective variable metric evolution strategy. In D. V. Arnold, T. Jansen, J. E. Rowe, and M. D. Vose (Eds.), Theory of Evolutionary Algorithms, Number 06061 in Dagstuhl Seminar Proceedings, Abstract Collection. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl.
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Igel, C. (2006a). The bias-invariance dilemma. In K. Bellman, P. Hofmann, C. Müller-Schloer, H. Schmeck, and R. Würtz (Eds.), Organic Computing - Controlled Emergence, Number 06031 in Dagstuhl Seminar Proceedings, Abstract Collection. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl.
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Gepperth, A. and S. Roth (2006). Applications of multi-objective structure optimization. Neurocomputing 6(7-9), 701-713.
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Glasmachers, T. and C. Igel (2006). Maximum-gain working set selection for support vector machines. Journal of Machine Learning Research 7, 1437-1466.
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Igel, C., T. Suttorp, and N. Hansen (2006). A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 453-460. ACM Press.
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Mersch, B., T. Glasmachers, P. Meinicke, and C. Igel (2006). Evolutionary optimization of sequence kernels for detection of bacterial gene starts. In Kollias et al. (Eds.), International Conference on Artificial Neural Networks (ICANN 2006), Number 4132 in LNCS, pp. 827-836. Springer-Verlag.
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Meyer, J., C. Igel, and D. Jancke (2006). Modelling dynamic activity patterns in early visual cortex based on voltage sensitive dye experiments. In K.-A. Hossmann (Ed.), Symposium `Neuro-Visionen 3', Perspektiven in Nordrhein-Westfalen, pp. 193-195. Verlag Ferdinand Schöningh.
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Roth, S., A. Gepperth, and C. Igel (2006). Multi-objective neural network optimization for visual object detection. In Y. Jin (Ed.), Multi-objective Machine Learning, Volume 16 of Studies in Computational Intelligence, pp. 629-655. Springer-Verlag.
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Suttorp, T. and C. Igel (2006). Multi-objective optimization of support vector machines. In Y. Jin (Ed.), Multi-objective Machine Learning, pp. 199-220. Springer-Verlag.
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Chalimourda, A., B. Schölkopf, and A. J. Smola (2005). Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks 18(2), 205.
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Friedrichs, F. and C. Igel (2005). Evolutionary tuning of multiple SVM parameters. Neurocomputing 64(C), 107-117.
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Friedrichs, F. and M. Schmitt (2005). On the power of boolean computations in generalized RBF neural networks. Neurocomputing 63, 483-498.
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Gepperth, A. and S. Roth (2005). Applications of multi-objective structure optimization. In M. Verleysen (Ed.), 13th European Symposium on Artificial Neural Networks (ESANN 2005), pp. 279-284. Evere, Belgium: d-side publications.
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Glasmachers, T. and C. Igel (2005a). Gradient-based adaptation of general gaussian kernels. Neural Computation 17(10), 2099-2105.
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Hüsken, M., Y. C. Jin, and B. Sendhoff (2005). Structure optimization of neural networks for evolutionary design optimization. Soft Computing 9(1), 21-28. Special Issue on Approximation and Learning in Evolutionary Computation.
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Igel, C., S. Roth@neuroinformatik.ruhr-uni-bochum.de">Wiegand, and F. Friedrichs (2005). Evolutionary optimization of neural systems: The use of self-adptation. In Trends and Applications in Constructive Approximation, Number 151 in International Series of Numerical Mathematics, pp. 103-123. Birkhäuser Verlag.
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Igel, C., M. Toussaint, and W. Weishui (2005). Rprop using the natural gradient. In Trends and Applications in Constructive Approximation, Number 151 in International Series of Numerical Mathematics, pp. 259-272. Birkhäuser Verlag.
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Igel, C. (2005). Multi-objective model selection for support vector machines. In C. A. C. Coello, E. Zitzler, and A. H. Aguirre (Eds.), Third International Conference on Evolutionary Multi-Criterion Optimization (EMO 2005), Volume 3410 of LNAI, pp. 534-546. Springer-Verlag.
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Igel, C. and B. Sendhoff (2005). Synergies between evolutionary and neural computation. In M. Verleysen (Ed.), 13th European Symposium on Artificial Neural Networks (ESANN 2004), pp. 241-252. Evere, Belgien: d-side publications.
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Mersch, B., N. Pfeifer, T. Glasmachers, P. Meinicke, and C. Igel (2005). Optimized sequence kernels for prediction of translation initiation sites. Technical Report IRINI 2005-01, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum.
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Niehaus, J., C. Igel, and W. Banzhaf (2005). Reducing the number of fitness evaluations in graph genetic programming using a canonical graph indexed database. Technical Report IRINI 2005-02, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum.
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Glasmachers, T. and C. Igel (2005b). Maximum-gain working set selection for support vector machines. Technical Report IRINI 2005-03, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum.
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Igel, C., N. Hansen, and S. Roth (2005). The multi-objective variable metric evolution strategy, Part I. Technical Report IRINI 2005-04, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum.
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Pellecchia, A., C. Igel, J. Edelbrunner, and G. Schöner (2005). Making driver modeling attractive. IEEE Intelligent Systems 20(2), 8-12.
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Winter, S., B. Brendel, and C. Igel (2005a). Registration of bone structures in 3D ultrasound and CT data: Comparison of different optimization strategies. In H. U. Lemke, K. Inamura, K. Doi, M. W. Vannier, and A. G. Farman (Eds.), Computer Assisted Radiology and Surgery (CARS 2005), Volume 1281 of International Congress Series, pp. 242-247. Elsevier.
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Winter, S., B. Brendel, and C. Igel (2005b). Registrierung von Knochen in 3D-Ultraschall- und CT-Daten: Vergleich verschiedener Optimierungsverfahren. In Bildverarbeitung für die Medizin (BVM), pp. 345-149. Springer-Verlag.
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Chalimourda, A., B. Schölkopf, and A. J. Smola (2004). Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks 17(1), 127-141.
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Igel, C. (2004). Recent results on no-free-lunch for optimization. In H. Beyer, T. Jansen, C. Reeves, and M. D. Vose (Eds.), Theory of Evolutionary Algorithms, Number 04081 in Dagstuhl Seminar Proceedings, Abstract Collection. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl.
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Friedrichs, F. and C. Igel (2004). Evolutionary tuning of multiple SVM parameters. In M. Verleysen (Ed.), 12th European Symposium on Artificial Neural Networks (ESANN 2004), pp. 519-524. Evere, Belgien: d-side publications.
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Igel, C. and M. Toussaint (2004). A No-Free-Lunch theorem for non-uniform distributions of target functions. Journal of Mathematical Modelling and Algorithms  4(3), 313-322.
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Igel, C. and K.-H. Temme (2004). The chaining syllogism in fuzzy logic. IEEE Transactions on Fuzzy Systems 12(6), 849-853.
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Schneider, S., C. Igel, C. Klaes, H. Dinse, and J. Wiemer (2004). Evolutionary adaptation of nonlinear dynamical systems in computational neuroscience. Journal of Genetic Programming and Evolvable Machines  5(2), 215-227.
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Toussaint, M. (2004b). Learning a world model and planning with a self-organizing, dynamic neural system. In Advances in Neural Information Processing Systems 16 (NIPS 2003), pp. 929-936. MIT Press, Cambridge.
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Toussaint, M. (2004a). The evolution of genetic representations and modular neural adaptation. Logos Verlag.
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Wiebringhaus, T., C. Igel, and J. Gebert (2004). Protein fold class prediction using neural networks with tailored early-stopping. In International Joint Conference on Neural Networks (IJCNN 2004), pp. 1693-1697. IEEE Press.
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Roth@neuroinformatik.ruhr-uni-bochum.de">Wiegand, S., C. Igel, and U. Handmann (2004b). Evolutionary optimization of neural networks for face detection. In 12th European Symposium on Artificial Neural Networks (ESANN 2004), pp. 139-144. Evere, Belgium: d-side publications.
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Roth@neuroinformatik.ruhr-uni-bochum.de">Wiegand, S., C. Igel, and U. Handmann (2004a). Evolutionary multi-objective optimisation of neural networks for face detection. International Journal of Computational Intelligence and Applications 4(3), 237-253.
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Bücher, T., C. Curio, H. Edelbrunner, C. Igel, D. Kastrup, I. Leefken, G. Lorenz, A. Steinhage, and W. von Seelen (2003). Image processing and behaviour planning for intelligent vehicles. IEEE Transactions on Industrial Electronics 50(1), 62-75.
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Hüsken, M. and P. Stagge (2003). Recurrent neural networks for time series classification. Neurocomputing 50(C), 223-235.
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Igel, C. and M. Toussaint (2003b). On classes of functions for which No Free Lunch results hold. Information Processing Letters 86(6), 317-321.
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Igel, C. (2003a). Beiträge zum Entwurf neuronaler Systeme. Aachen: Shaker Verlag.
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Igel, C. (2003b). Neuroevolution for reinforcement learning using evolution strategies. In R. Sarker, R. Reynolds, H. Abbass, K. C. Tan, B. McKay, D. Essam, and T. Gedeon (Eds.), Congress on Evolutionary Computation (CEC 2003), Volume 4, pp. 2588-2595. IEEE Press.
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Igel, C. and M. Hüsken (2003). Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing 50(C), 105-123.
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Igel, C. and M. Kreutz (2003). Operator adaptation in evolutionary computation and its application to structure optimization of neural networks. Neurocomputing 55(1-2), 347-361.
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Igel, C. and M. Toussaint (2003a). Neutrality and self-adaptation. Natural Computing 2(2), 117-132.
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Jin, Y. C., M. Hüsken, and B. Sendhoff (2003). Quality measures for approximate models in evolutionary computation. In Proceedings of 2003 GECCO Workshop on Learning, Adaptation and Approximation in Evolutionary Computation, Chicago.
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Mayr, T., C. Igel, G. Liebsch, I. Klimant, and O. S. Wolfbeis (2003). Cross-reactive metal ion sensor array in a microtiterplate format. Analytical Chemistry 75(17), 4389-4396.
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Toussaint, M. (2003b). On the evolution of phenotypic exploration distributions. In C. Cotta, K. De Jong, R. Poli, and J. Rowe (Eds.), Foundations of Genetic Algorithms 7 (FOGA VII), pp. 169-182. Morgan Kaufmann.
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Toussaint, M. (2003c). The structure of evolutionary exploration: On crossover, buildings blocks, and Estimation-Of-Distribution algorithms. In 2003 Genetic and Evolutionary Computation Conference (GECCO 2003), pp. 1444-1456.
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Toussaint, M. (2003a). Demonstrating the evolution of complex genetic representations: An evolution of artificial plants. In 2003 Genetic and Evolutionary Computation Conference (GECCO 2003), pp. 86-97.
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Wiebringhaus, T., U. Faigle, D. Schomburg, J. Gebert, C. Igel, and G.-W. Weber (2003). Protein fold class prediction using neural networks reconsidered. In Currents in Computational Molecular Biology, The Seventh Annual International Conference on Research in Computational Molecular Biology (RECOMB 2003), pp. 225-226.
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Dinse, H. R., M. Hüsken, C. Igel, C. Klaes, M. Nunkesser, S. Schneider, and J. Wiemer (2002). Derandomized evolution strategies in computational neuroscience. In W. Banzhaf and J. A. Foster (Eds.), Biological Applications of Genetic and Evolutionary Computation (BioGEC 2002) - A Bird-of-a-feather Workshop at the Genetic and Evolutionary Computation Conference (GECCO 2002).
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Hüsken, M. and C. Igel (2002). Balancing learning and evolution. In W. B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska (Eds.), GECCO-2002: Proceedings of the Generic and Evolutionary Computation Conference, San Francisco, CA 94104, USA, pp. 391-398. Morgan Kaufmann Publishers.
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Hüsken, M., C. Igel, and M. Toussaint (2002). Task-dependent evolution of modularity in neural networks. Connection Science 14(3), 219-229.
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Hüsken, M., Y. Jin, and B. Sendhoff (2002). Structure optimization of neural networks for evolutionary design optimization. In A. M. Barry (Ed.), GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, 445 Burgess Drive, Menlo Park, CA 94025, pp. 13-16. AAAI, Menlo Park, CA, USA.
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Igel, C. and P. Stagge (2002a). Effects of phenotypic redundancy in structure optimization. IEEE Transactions on Evolutionary Computation 6(1), 74-85.
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Igel, C., W. von Seelen, W. Erlhagen, and D. Jancke (2002). Evolving field models for inhibition effects in early vision. Neurocomputing 44-46(C), 467-472.
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Igel, C. and P. Stagge (2002b). Graph isomorphisms affect structure optimization of neural networks. In International Joint Conference on Neural Networks 2002 (IJCNN), pp. 142-147. IEEE Press.
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Toussaint, M. (2002b). On model selection and the disability of neural networks to decompose tasks. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), pp. 245-250.
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Toussaint, M. (2002a). A neural model for multi-expert architectures. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), pp. 2755-2760.
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Toussaint, M. and C. Igel (2002). Neutrality: A necessity for self-adaptation. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), pp. 1354-1359.
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Weinert, K., O. Webber, M. Hüsken, J. Mehnen, and W. Theis (2002). Analysis and prediction of dynamic disturbances of the BTA deep hole drilling process. In Proceedings of the 3rd CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (ICME 2002).
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Bergener, T., C. Bruckhoff, and C. Igel (2001). Parameter optimization for visual obstacle detection using a derandomized evolution strategy. In J. Blanc-Talon and D. Popesc (Eds.), Imaging and Vision Systems: Theory, Assessment and Applications, Volume 9 of Advances in Computation: Theory and Practice, Chapter 13, pp. 265-279. Huntington, NY 11743 (USA): NOVA Science Books.
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Busse, A., M. Hüsken, and P. Stagge (2001). Offline-Analyse eines BTA-Tiefbohrprozesses. Technical Report 16/01, SFB 475, Fachbereich Statistik, Universität Dortmund, 44221 Dortmund, Deutschland.
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Edelbrunner, H., U. Handmann, C. Igel, I. Leefken, and W. von Seelen (2001). Application and optimization of neural field dynamics for driver assistance. In The IEEE 4th International Conference on Intelligent Transportation Systems (ITSC '01), Piscataway, NJ, pp. 309-314. IEEE Press.
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Hüsken, M., C. Igel, and M. Toussaint (2001). Task-dependent evolution of modularity in neural networks - a quantitative case study. In E. D. Goodman (Ed.), 2001 Genetic and Evolutionary Computation Conference (GECCO 2001) - Late-Breaking Papers, pp. 187-193.
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Igel, C., W. Erlhagen, and D. Jancke (2001). Optimization of neural field models. Neurocomputing 36(1-4), 225-233.
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Igel, C. and W. von Seelen (2001). Design of a field model for early vision: A case study of evolutionary algorithms in neuroscience. In 28th Göttingen Neurobiology Conference, Volume 2, pp. 1034. Georg Thieme Verlag.
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Igel, C. and M. Kreutz (2001). Operator adaptation in structure optimization of neural networks. In L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. Garzon, and E. Burke (Eds.), Genetic and Evolutionary Computation Conference (GECCO 2001), San Francisco, pp. 1094. Morgan Kaufmann.
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Ronnewinkel, C., C. O. Wilke, and T. Martinetz (2001). Genetic algorithms in time-dependent environments. In L. Kallel, B. Naudts, and A. Rogers (Eds.), Theoretical Aspects of Evolutionary Computing, Natural Computing Series. Springer Verlag.
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Ronnewinkel, C. and T. Martinetz (2001). Explicit speciation with few a priori parameters for dynamic optimization problems. GECCO 2001, Workshop on `Evolutionary Algorithms for Dynamic Optimization Problems'.
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Stagge, P. (2001). Strukturoptimierung rückgekoppelter neuronaler Netze. Konzepte neuronaler Informationsverarbeitung. Stuttgart: ibidem-Verlag.
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Stagge, P. and C. Igel (2001). Structure optimization and isomorphisms. In L. Kallel, B. Naudts, and A. Rogers (Eds.), Theoretical Aspects of Evolutionary Computing, Natural Computing series, pp. 409-422. Springer-Verlag.
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Toussaint, M. (2001). Self-adaptive exploration in evolutionary search. Internal Report IRINI 01-05, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany. Los Alamos e-Print Archive (http://arXiv.org/abs/physics/0102009).
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Jin, Y. C., W. von Seelen, and B. Sendhoff (1999). On generating fc3 fuzzy rule systems from data using evolution strategies. IEEE Transactions Systems, Man and Cybernetics, Part B: Cybernetics 29(4), 829-845.
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Goerick, C. (1998a). Beiträge zur Theorie der Lerndynamik künstlicher neuronaler Netze. Number 557 in Informatik und Kommunikationstechnik. VDI Verlag GmbH.
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Goerick, C. (1998b). Considerations of the gain spectrum. Internal Report IRINI 98-02, Institut für Neuroinformatik.
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Igel, C. and M. Kreutz (1998). Operator adaptation in evolutionary computation and its application to structure optimization of neural networks. Internal Report IRINI 01-03, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany.
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Igel, C. (1998). Causality of hierarchical variable length representations. In D. B. Fogel, H.-P. Schwefel, T. Bäck, and X. Yao (Eds.), Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC'98), pp. 324-329. IEEE Press.
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Jin, Y. C., W. von Seelen, and B. Sendhoff (1998). An approach to rule-based knowledge extraction. In IEEE International Conference on Fuzzy Systems, Anchorage, Alaska, pp. 1188-1993.
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Sendhoff, B. and M. Kreutz (1998). Evolutionary optimization of the structure of neural networks using a recursive mapping as encoding. In G. Smith, N. Steele, and R. Albrecht (Eds.), Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA'97), pp. 370-374. Springer Verlag.
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Zhuang, Q., M. Kreutz, and J. Gayko (1998a). Optimization of a fuzzy system using evolutionary algorithms. In W. Brauer (Ed.), Proceedings of the Fuzzy-Neuro-Systems 98, pp. 178-185.
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Zhuang, Q., M. Kreutz, and J. E. Gayko (1998c). Optimization of a fuzzy controller for a driver assistant system. In W. Brauer (Ed.), Proceedings of the Fuzzy-Neuro-Systems 98, pp. 376-382.
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Gayko, J. E., R. Lohmann, H. Voss, B. Sendhoff, and T. Zamzow (1997). Application of structure evolution to system state diagnosis. In Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN'97), Stockholm.
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Kreutz, M., C. Sievers, A. Dietrich, and B. Sendhoff (1997). A programmers guide to PAMPER - classes & tools - (P)arallel (A)nd (M)odular (P)rogramming (E)nvi(r)onment. Internal Report IRINI 97-03, Institut für Neuroinformatik.
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Pötter, C. (1997). Evolutionary learning of autonomous agents with anticipatory capabilities. In First International Conference on Computing Anticipatory Systems (CASYS'97), Liege, Belgium.
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Sendhoff, B., M. Kreutz, and W. von Seelen (1997b). A condition for the genotype-phenotype mapping: Causality. In T. Bäck (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA'97), San Francisco, USA.
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Sendhoff, B., M. Kreutz, and W. von Seelen (1997a). Causality and the analysis of local search in evolutionary algorithms. Internal Report IRINI 97-16, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany.
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Sievers, C., M. Kreutz, and B. Sendhoff (1997). Modulverwaltung und Kommunikation in PAMPER - (P)arallel (A)nd (M)odular (P)rogramming (E)nvi(r)onment. Internal Report IRINI 97-04, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany.
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Stagge, P. and B. Sendhoff (1997). An extended elman net for modeling time series. In W. Gerstner, A. Germond, M. Hasler, and J. Nicoud (Eds.), Proceedings of the International Conference on Artificial Neural Networks (ICANN'97), Volume 1327 of Lecture Notes in Computer Science, pp. 427-432. Springer Verlag.
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Zhuang, Q. and M. Kreutz (1997). Optimierung eines Fuzzy-Sugeno-Systems. Internal Report IRINI 97-11, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany.
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Gayko, J. E. and C. Goerick (1996). Artificial neural networks for tyre pressure estimation. In Proceedings of the International Conference on Engineering Applications of Neural Networks (EANN'96), London, GB.
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Sendhoff, B. and M. Kreutz (1996). Analysis of possible genome-dependence of mutation rates in genetic algorithms. In T. C. Fogarty (Ed.), Evolutionary Computing - Selected Papers from the 1996 AISB Workshop, Volume 1134 of Lecture Notes in Computer Science, pp. 257-269. Springer Verlag.
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Wienholt, W. (1995). Stapeloptimierung einer Haubenglühanlage mithilfe von Evolutionsstrategien - eine Machbarkeitsuntersuchung. Internal report, Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany.
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Voß, T., N. Hansen, and C. Igel. Recombination for learning strategy parameters in the MO-CMA-ES. In X. Gandibleux and C. Fonseca (Eds.), Fifth International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), LNCS. Springer-Verlag. In press.
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