2017

 

Aufsatz (Zeitschrift)

Ahrari, A., Deb, K., & Preuss, M. (2017). Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations. Evolutionary Computation 25(3): 439-471 (2017), 25(3), 439–471.
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Bossek, J. (2017). mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. The Journal of Open Source Software, 2017. (Submitted)
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Bossek, J. (2017). smoof: Single- and Multi-Objective Optimization Test Functions. The R Journal, 2017(1). (Accepted)
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Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2017). OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 2017, 1–15.
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Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2017). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation Journal, 2017. (In press)
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Aufsatz (Konferenz)

Bossek, J., & Grimme, C. (2017). An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Honolulu, Hawaii. (Accepted)
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Bossek, J., & Grimme, C. (2017). A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Honolulu, Hawai. (Accepted)
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Bossek, J. (2017). ecr 2.0: A Modular Framework for Evolutionary Computation in R. In Proceedings of the Genetic and Evolutionary Computation Conference, Berlin, Germany. (Accepted)
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Carnein, M., Assenmacher, D., & Trautmann, H. (2017). An Empirical Comparison of Stream Clustering Algorithms. In Proceedings of the ACM International Conference on Computing Frontiers (CF '17), Siena, Italy, 361–365.
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Carnein, M., Assenmacher, D., & Trautmann, H. (2017). Stream Clustering of Chat Messages with Applications to Twitch Streams. In de Cesare, S., & Ulrich, F. (Eds.), Advances in Conceptual Modeling: ER 2017 Workshops AHA, MoBiD, MREBA, OntoCom, and QMMQ, Valencia, Spain, November 6--9, 2017, Proceedings (pp. 79–88). Cham: Springer International Publishing. (Accepted)
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Carnein, M., Heuchert, M., Homann, L., Trautmann, H., Vossen, G., Becker, J., & Kraume, K. (2017). Towards Efficient and Informative Omni-Channel Customer Relationship Management. In de Cesare, S., & Ulrich, F. (Eds.), Advances in Conceptual Modeling: ER 2017 Workshops AHA, MoBiD, MREBA, OntoCom, and QMMQ, Valencia, Spain, November 6--9, 2017, Proceedings (pp. 69–78). Cham: Springer International Publishing. (Accepted)
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Carnein, M., Homann, L., Trautmann, H., Vossen, G., & Kraume, K. (2017). Customer Service in Social Media — An Empirical Study of the Airline Industry. In Proceedings of the 17th Conference on Database Systems for Business, Technology, and Web (BTW '17), Stuttgart, Germany, 33–40.
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Hanster, C., & Kerschke, P. (2017). flaccogui: Exploratory Landscape Analysis for Everyone. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17) Companion, Berlin, Germany, 1215–1222.
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Kerschke, P., & Grimme, C. (2017). An Expedition to Multimodal Multi-Objective Optimization Landscapes. In Proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO), Münster, Germany, 329–343.
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Kerschke, P., & Preuss, M. (2017). Exploratory Landscape Analysis: Advanced Tutorial at GECCO 2017. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '17) Companion, Berlin, Germany, 762–781.
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Khalifa, A., Preuss, M., & Togelius, J. (2017). Multi-objective Adaptation of a Parametrized GVGAI Agent Towards Several Games. In Proceedings of the EMO 2017, Münster, 359–374.
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Lang, M., & Grimme, C. (2017). Towards Standardized and Seamless Integration of Expert Knowledge into Multi-objective Evolutionary Optimization Algorithms. In Proceedings of the Evolutionary Multi-Criterion Optimization, Münster, 375–389.
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Buchbeitrag (Sammel-, Herausgeberband)

Adrián, S. H. V., Lara, A., Trautmann, H., Rudolph, G., & Schütze, O. (2017). The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems. In Schütze, O., Trujillo, L., Legrand, P., & Maldonado, Y. (Eds.), NEO 15 (pp. 281–330). Cham: Springer International Publishing.
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Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N., & Emmerich, M. (2017). Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms. In Trautmann, H., Rudolph, G., Klamroth, K., Schütze, O., Wiecek, M., Jin, Y., & Grimme, C. (Eds.), Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings (pp. 406–421). Cham: Springer International Publishing.
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Tierney, K., Handali, J., Grimme, C., & Trautmann, H. (2017). Multi-objective Optimization for Liner Shipping Fleet Repositioning. In Trautmann, H., Rudolph, G., Klamroth, K., Schütze, O., Wiecek, M., Jin, Y., & Grimme, C. (Eds.), Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings (pp. 622–638). Cham: Springer International Publishing.
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Bericht (Arbeits-, Forschungs-, Jahresbericht)

Trautmann, H., Vossen, G., Homann, L., Carnein, M., & Kraume, K. (2017). Challenges of Data Management and Analytics in Omni-Channel CRM. In Becker, J., Backhaus, K., Dugas, M., Hellingrath, B., Hoeren, T., Klein, S., Kuchen, H., Trautmann, H., & Vossen, G. (Eds.), ERCIS Working Papers: Vol. 28. Münster: European Research Center for Information Systems.
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Sonstige (technische Spezifikation, informelle Veröffentlichung)

Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M. (2017). mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions.
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Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2017). OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML.
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Kerschke, P. (2017). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco.
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2016

 

Aufsatz (Zeitschrift)

Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M., Malitsky, Y., Fréchette, A., Hoos, H. H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2016). ASlib: A Benchmark Library for Algorithm Selection. Artificial Intelligence Journal, 237, 41–58.
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Buro, M., Ontanón, S., & Preuss, M. (2016). Guest Editorial Real-Time Strategy Games. IEEE Transactions on Computational Intelligence and AI in Games, 8(4), 317–318.
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Neumann, F., & Trautmann, H. (2016). Working Group Report: Bridging the Gap Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of Evolutionary Algorithms (Dagstuhl Seminar 15211). Dagstuhl Reports, 5(5), 78–79.
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Rudolph, G., Schütze, O., Grimme, C., Domínguez-Medina, C., & Trautmann, H. (2016). Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results. Computational Optimization and Applications, 64(2), 589–618.
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Schütze, O., Sosa, H. V., Trautmann, H., & Rudolph, G. (2016). The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems. Journal of Heuristics, 22(3), 273–300.
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Wessing, S., & Preuss, M. (2016). On multiobjective selection for multimodal optimization. Computational Optimization and Applications, 63(3), 875–902.
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Aufsatz (Konferenz)

Beyer, M., Agureikin, A., Anokhin, A., Laenger, C., Nolte, F., Winterberg, J., Renka, M., Rieger, M., Pflanzl, N., Preuss, M., & Volz, V. (2016). An Integrated Process for Game Balancing. In Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2016), Santorini, Greece. (Accepted)
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Blot, A., Hoos, H., Jourdan, L., Marmion, M., & Trautmann, H. (2016). MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In Proceedings of the Learning and Intelligent Optimization, 10th International Conference, Ischia. (Accepted)
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Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers. In Proceedings of the 10th International Conference on Learning and Intelligent Optimization, Ischia, Italy. (Accepted)
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Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. In Proceedings of the XVth International Conference of the Italian Association for Artificial Intelligence, Genova, Italy, 3–12.
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Carnein, M., Schöttle, P., & Böhme, R. (2016). Telltale Watermarks for Counting JPEG Compressions. In Proceedings of the IS&T Electronic Imaging: Media Watermarking, Security, and Forensics (EI '16), San Francisco, CA, 1–10.
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Horn, H., Volz, V., Perez, L. D., & Preuss, M. (2016). MCTS/EA hybrid GVGAI players and game difficulty estimation. In Proceedings of the Computational Intelligence and Games, Santorini, 1–8.
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Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '16), Denver, CO, USA, 229–236.
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Kerschke, P., & Trautmann, H. (2016). The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Kanada.
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Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes. In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), Edinburgh, Scotland, 962–972.
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Buchbeitrag (Sammel-, Herausgeberband)

Rudolph, G., Schütze, O., & Trautmann, H. (2016). On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front. In Squillero, G., & Burelli, P. (Eds.), Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II (pp. 42–55). Cham: Springer International Publishing.
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2015

 

Aufsatz (Zeitschrift)

Brockhoff, D., Wagner, T., & Trautmann, H. (2015). R2 Indicator Based Multiobjective Search. Evolutionary Computation Journal, 23(3), 369–395.
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Lucas, S., Mateas, M., Preuss, M., Spronck, P., & Togelius, J. (2015). Artificial and Computational Intelligence in Games: Integration (Dagstuhl Seminar 15051). Dagstuhl Reports, 5(1), 207–242.
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Mersmann, O., Preuss, M., Trautmann, H., Bischl, B., & Weihs, C. (2015). Analyzing the BBOB Results by Means of Benchmarking Concepts. Evolutionary Computation Journal, 23(1), 161–185.
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Preuss, M., & Rudolph, G. (2015). Conference report on IEEE CIG 2014 [Conference reports]. IEEE Computational Intelligence Magazine, 10(1), 14–15.
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Aufsatz (Konferenz)

Bossek, J., Bischl, B., Wagner, T., & Rudolph, G. (2015). Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. In Proceedings of the Genetic and Evolutionary Computation Conference, Madrid, Spanien.
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Carnein, M., Schöttle, P., & Böhme, R. (2015). Forensics of High-Quality JPEG Images with Color Subsampling. In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS '15), Rome, Italy, 1–6.
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Chinnov, A., Meske, C., Kerschke, P., Stieglitz, S., & Trautmann, H. (2015). An Overview of Topic Discovery in Twitter Communication through Social Media Analytics. In Proceedings of the 20th Americas Conference on Information Systems (AMCIS '15), Puerto Rico, 1–10.
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Grimme, C., Meisel, S., Trautmann, H., Rudolph, G., & Wölck, M. (2015). Multi-Objective Analysis of Approaches to Dynamic Routing Of a Vehicle. In Proceedings of the European Conference On Information Systems, Münster, Germany.
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Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2015). Detecting Funnel Structures by Means of Exploratory Landscape Analysis. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain, 265–272.
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Kotthoff, L., Kerschke, P., Hoos, H. H., & Trautmann, H. (2015). Improving the State of the Art in Inexact TSP Solving using Per-Instance Algorithm Selection. In Dhaenens, C., Jourdan, L., & Marmion, M.-E. (Eds.), Learning and Intelligent Optimization, 9th International Conference (pp. 202–217). Cham: Springer International Publishing.
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Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. In Proceedings of the Genetic and Evolutionary Computation Conference, Madrid, Spanien, 425–432.
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Quadflieg, J., Rudolph, G., & Preuss, M. (2015). How costly is a good compromise: Multi-objective TORCS controller parameter optimization. In Proceedings of the CIG 2015, Tainan, Taiwan, 454–460.
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Sosa, H. V., Schütze, O., Trautmann, H., & Rudolph, G. (2015). On the Behavior of Stochastic Local Search within Parameter Dependent MOPs. In Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, Guimaraes, Portugal, 126–140.
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Stammer, D., Mannheim, H., Gunther, T., & Preuss, M. (2015). Player-Adaptive Spelunky level generation. In Proceedings of the 2015 IEEE Conference on Computational Intelligence and Games, CIG 2015, Tainan, Taiwan, 130–137.
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Buch (Monographie)

Preuss, M. (2015). Multimodal Optimization by Means of Evolutionary Algorithms. Natural Computing Series. Springer.
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Terveer, I. (2015). Mathematik Formeln — Wirtschaftswissenschaften (1.). Konstanz und München: UVK.
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Buchbeitrag (Sammel-, Herausgeberband)

Preuss, M., Wessing, S., Rudolph, G., & Sadowski, G. (2015). Solving Phase Equilibrium Problems by Means of Avoidance-Based Multiobjectivization. In Kacprzyk, J., & Pedrycz, W. (Eds.), Springer Handbook of Computational Intelligence (pp. 1159–1171). Springer.
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Kurzbeitrag (Poster)

Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015). Averaged Hausdorff Approximations of Pareto Fronts Based on Multiobjective Estimation of Distribution Algorithms. Poster session presented at the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain.
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Sonstige (technische Spezifikation, informelle Veröffentlichung)

Bischl, B., Kerschke, P., Kotthoff, L., Lindauer, M. T., Malitsky, Y., Fréchette, A., Hoos, H. H., Hutter, F., Leyton-Brown, K., Tierney, K., & Vanschoren, J. (2015). ASlib: A Benchmark Library for Algorithm Selection.
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Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015). Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms.
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2014

 

Aufsatz (Zeitschrift)

Liboschik, T., Kerschke, P., Fokianos, K., & Fried, R. (2014). Modelling interventions in INGARCH processes. International Journal of Computer Mathematics, 0(ja), 1–19.
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Quadflieg, J., Preuss, M., & Rudolph, G. (2014). Driving as a human: A track learning based adaptable architecture for a car racing controller. Genetic Programming and Evolvable Machines, 15(4), 433–476.
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Aufsatz (Konferenz)

Burelli, P., & Preuss, M. (2014). Automatic camera control: A dynamic multi-objective perspective. In Proceedings of the 17th European Conference on Applications of Evolutionary Computation, EvoApplications 2014, Granada, Espania, 361–373.
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Carnein, M., Quiring, E., Haack, A., Möhring, A., & Becker, J. (2014). Laiengerechte Erzeugung von 3D-Animationen am Beispiel von textuellen Unfallbeschreibungen. In Proceedings of the 17th International Legal Informatics Symposium (IRIS '14), Salzburg, Austria, 473–480.
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Carnein, M., Schöttle, P., & Böhme, R. (2014). Predictable Rain? Steganalysis of Public-key Steganography Using Wet Paper Codes. In Unterweger, A., Uhl, A., Katzenbeisser, S., Kwitt, R., & Piva, A. (Eds.), Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security (IH — MMSec '14) (pp. 97–108). New York, NY, USA: ACM.
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Preuss, M., Liapis, A., & Togelius, J. (2014). Searching for good and diverse game levels. In Proceedings of the 2014 IEEE Conference on Computational Intelligence and Games, CIG 2014, Dortmund, Deutschland.
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Preuss, M., Voll, P., Bardow, A., & Rudolph, G. (2014). Looking for alternatives: Optimization of energy supply systems without superstructure. In Proceedings of the 17th European Conference on Applications of Evolutionary Computation, EvoApplications 2014, Granada, Espania, 177–188.
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Rudolph, G., Grimme, C., Schütze, O., & Trautmann, H. (2014). An Aspiration Set EMOA Based on Averaged Hausdorff Distances. In Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8), Gainesville, Florida, USA, 153–156.
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Wessing, S., Preuss, M., & Trautmann, H. (2014). Stopping Criteria for Multimodal Optimization. In Proceedings of the Parallel Problem Solving from Nature — PPSN XIII, Ljubljana, Slovenia, 141–150.
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Buchbeitrag (Sammel-, Herausgeberband)

Kerschke, P., Preuss, M., Hernández, C., Schütze, O., Sun, J.-Q., Grimme, C., Rudolph, G., Bischl, B., & Trautmann, H. (2014). Cell Mapping Techniques for Exploratory Landscape Analysis. In Tantar, A.-A., Tantar, E., Sun, J.-Q., Zhang, W., Ding, Q., Schütze, O., Emmerich, M. T. M., Legrand, P., Del, M. P., & Coello, C. C. A. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V (pp. 115–131). Advances in Intelligent Systems and Computing: Vol. 288. Cham: Springer International Publishing.
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Rudolph, G., Schütze, O., Grimme, C., & Trautmann, H. (2014). A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets. In Tantar, A., Tantar, E., Sun, J., Zhang, W., Ding, Q., Schütze, O., Emmerich, M., Legrand, P., Del, M. P., & Coello, C. C. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V (pp. 261–273). Advances in Intelligent Systems and Computing: Vol. 288. Springer International Publishing.
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2013

 

Aufsatz (Zeitschrift)

Grimme, C., Lepping, J., & Schwiegelshohn, U. (2013). Multi-criteria scheduling: an agent-based approach for expert knowledge integration. Journal of Scheduling, 16(4), 369–383.
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Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. Annals of Mathematics and Artificial Intelligence, 69, 151–182.
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Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Annals of Mathematics and Artificial Intelligence (Annals of Mathematics and Artificial Intelligence), 69(2), 151–182.
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Ontañón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., & Preuss, M. (2013). A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft. IEEE Trans. Comput. Intellig. and AI in Games, 5(4), 293–311.
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Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., Yannakakis, G., & Grappiolo, C. (2013). Controllable procedural map generation via multiobjective evolution. Genetic Programming and Evolvable Machines, 14(2), 1–33.
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Trautmann, H., Wagner, T., Biermann, D., & Weihs, C. (2013). Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index. Journal of Multi-Criteria Decision Analysis, 20(5-6), 319–337.
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Aufsatz (Konferenz)

Dominguez-Medina, C., Rudolph, G., Schütze, O., & Trautmann, H. (2013). Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, 3190–3197.
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Kuchem, M., Preuss, M., & Rudolph, G. (2013). Multi-objective assessment of pre-optimized build orders exemplified for StarCraft 2. In Proceedings of the 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013, Niagara Falls, ON, can, 1–8.
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Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., & Trautmann, H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. In Proceedings of the FOGA, Adelaide, Australia, 147–160.
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Preuss, M., Kozakowski, D., Hagelbäck, J., & Trautmann, H. (2013). Reactive strategy choice in StarCraft by means of Fuzzy Control. In Proceedings of the 2013 IEEE Conference on Computational Inteligence in Games (CIG), Niagara Falls, ON, Canada, 1–8.
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Preuss, M., Kozakowski, D., Hagelback, J., & Trautmann, H. (2013). Reactive strategy choice in StarCraft by means of Fuzzy Control. In Proceedings of the 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013, Niagara Falls, ON, can, 1–8.
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Preuss, M., & Wessing, S. (2013). Measuring multimodal optimization solution sets with a view to multiobjective techniques. In Proceedings of the EVOLVE, Leiden, nld, 123–137.
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Rudolph, G., Trautmann, H., Sengupta, S., & Schütze, O. (2013). Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation. In Purshouse, R., Fleming, P., Fonseca, C., Greco, S., & Shaw, J. (Eds.), Evolutionary Multi-Criterion Optimization — 7th International Conference, EMO 2013, Sheffield, UK, Proceedings (pp. 443–458). Lecture Notes in Computer Science: Vol. 7811. Springer.
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Sosa-Hernandez, V., Schütze, O., Rudoph, G., & Trautmann, H. (2013). Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms. In Proceedings of the GECCO 2013, Amsterdam (Netherlands), 1699–1702.
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Stoean, C., Preuss, M., & Stoean, R. (2013). EA-based parameter tuning of multimodal optimization performance by means of different surrogate models. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013, Amsterdam, nld, 1063–1070.
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Trautmann, H., Wagne, T., & Brockhoff, D. (2013). R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In Proceedings of the Learning and Intelligent Optimization Conference 7, Catania, Italy, 70–74.
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Wagner, T., Trautmann, H., & Brockhoff, D. (2013). Preference Articulation by Means of the R2 Indicator. In Purshouse, R. C., Fleming, P. J., Fonseca, C. M., Greco, S., & Shaw, J. (Eds.), Evolutionary Multi-Criterion Optimization — 7th International Conference, EMO 2013, Sheffield, UK, Proceedings (pp. 81–95). Lecture Notes in Computer Science: Vol. 7811. Springer.
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Wessing, S., Preuss, M., & Rudolph, G. (2013). Niching by multiobjectivization with neighbor information: Trade-offs and benefits. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, mex, 103–110.
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Buch (Monographie)

Terveer, I. (2013). Mathematik für Wirtschaftswissenschaften (3rd ed.). Konstanz, München: UVK Lucius.
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Buchbeitrag (Sammel-, Herausgeberband)

Bartz-Beielstein, T., & Preuss, M. (2013). Experimental Analysis of Optimization Algorithms: Tuning and Beyond. In Borenstein, Y., & Moraglio, A. (Eds.), Theory and Principled Methods for the Design of Metaheuristics (pp. 205–245). Springer.
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Grimme, C., Kemmerling, M., & Lepping, J. (2013). On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective Problems. In Tantar, E., Tantar, A., Bouvry, P., Del, M. P., Legrand, P., Coello, C. C., & Schütze, O. (Eds.), EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation (pp. 333–363). Studies in Computational Intelligence: Vol. 447. Springer Berlin Heidelberg.
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Kemmerling, M., Ackermann, N., & Preuss, M. (2013). Making diplomacy bots individual. In Hingston, P. (Ed.), Believable Bots: Can Computers Play Like People? (pp. 265–288). Springer-Verlag Berlin Heidelberg.
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Sosa, H. V., Schütze, O., Rudolph, G., & Trautmann, H. (2013). The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume. In Emmerich, M., Deutz, A., Schuetze, O., Bäck, T., Tantar, A., Moral, P., Legrand, P., Bouvry, P., & Coello, C. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV (pp. 189–205). Advances in Intelligent Systems and Computing: Vol. 227. Springer International Publishing.
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Trautmann, H., Rudolph, G., Dominguez-Medina, C., & Schütze, O. (2013). Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems. In Schütze, O., Coello, C. C., Tantar, A., Tantar, E., Bouvry, P., Del, M. P., & Legrand, P. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II (pp. 89–105). Advances in Intelligent Systems and Computing: Vol. 175. Springer Berlin Heidelberg.
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2012

 

Aufsatz (Zeitschrift)

Bischl, B., Mersmann, O., Trautmann, H., & Weihs, C. (2012). Resampling Methods in Model Validation. Evolutionary Computation Journal, 20(2), 249–275.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2012). Parallel Predator-Prey Interaction for Evolutionary Multi-Objective Optimization. Natural Computing, 11(3), 519–533.
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Lucas, S., Mateas, M., Preuss, M., Spronck, P., & Togelius, J. (2012). Artificial and Computational Intelligence in Games (Dagstuhl Seminar 12191). Dagstuhl Reports, 2(5), 43–70.
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Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local search and the traveling salesman problem: A feature-based characterization of problem hardness. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)), 7219 LNCS, 115–129.
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Ochoa, G., Bartz-Beielstein, T., Preuss, M., & Schoenauer, M. (2012). Editorial for the special issue on automated design and assessment of heuristic search methods. Evolutionary Computation, 20(2), 161–163.
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Rudolph, G., Trautmann, H., & Schütze, O. (2012). Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. at-Automatisierungstechnik, 60, 610–621.
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Vatolkin, I., Preuß, M., Rudolph, G., Eichhoff, M., & Weihs, C. (2012). Multi-objective evolutionary feature selection for instrument recognition in polyphonic audio mixtures. Soft Computing, 16(12), 2027–2047.
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Aufsatz (Konferenz)

Bartz-Beielstein, T., Preuss, M., & Zaefferer, M. (2012). Statistical analysis of optimization algorithms with R. In Proceedings of the GECCO 2012, Philadelphia, Pennsylvania, USA, 1259–1286.
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Bischl, B., Mersmann, O., Trautmann, H., & Preuss, M. (2012). Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In Soule, T., & Moore, J. (Eds.), Genetic and Evolutionary Computation Conference, GECCO '12, Philadelphia, PA, USA (pp. 313–320). ACM.
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Bischl, B., Mersmann, O., Trautmann, H., & Preuss, M. (2012). Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, GECCO'12, Philadelphia, PA, usa, 313–320.
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Brockhoff, D., Wagner, T., & Trautmann, H. (2012). On the Properties of the R2 Indicator. In Soule, T., & others, (Eds.), Proc. 14th Int'l. Genetic and Evolutionary Computation Conference (GECCO '12) (pp. 465–472). ACM.
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Jordan, A., Scheftelowitsch, D., Lahni, J., Hartwecker, J., Kuchem, M., Walter-Huber, M., Vortmeier, N., Delbrügger, T., Güler, &. V. I., & Preuss, M. (2012). BeatTheBeat music-based procedural content generation in a mobile game. In Proceedings of the CIG 2012, Granada, Spain, 320–327.
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Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Hamadi, Y., & Schoenauer, M. (Eds.), Learning and Intelligent Optimization — 6th International Conference, LION 6, Paris (pp. 115–129). Lecture Notes in Computer Science: Vol. 7219. Springer.
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Preuss, M. (2012). Improved topological niching for real-valued global optimization. In Proceedings of the EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, EvoApplications 2012, Malaga, esp, 386–395.
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Preuss, M., Burelli, P., & Yannakakis, G. (2012). Diversified virtual camera composition. In Proceedings of the EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, EvoApplications 2012, Malaga, esp, 265–274.
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Preuss, M., Wagner, T., & Ginsbourger, D. (2012). High-dimensional model-based optimization based on noisy evaluations of computer games. In Proceedings of the 6th International Conference on Learning and Intelligent Optimization, LION 6, Paris, fra, 145–159.
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Buch (Monographie)

Terveer, I. (2012). Fit für die Prüfung: Mathematik für Wirtschaftswissenschaften (1st ed.). Konstanz: UVK-Lucius.
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Buch (Sammel-, Herausgeberband)

Di, C. C., Agapitos, A., Cagnoni, S., Cotta, C., De, V. F., Di, C. G., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A., Farooq, M., Langdon, W., Merelo-Guervós, J.-J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A., Togelius, J., Urqhart, N., Uyar, A., & Yannakakis, G. (Eds.) (2012). Applications of Evolutionary Computation — EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, Málaga, Spain, April 11-13, 2012, Proceedings. LNCS: Vol. 7248. Springer.
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Buchbeitrag (Sammel-, Herausgeberband)

Grimme, C., & Lepping, J. (2012). An Approach to Instantly Use Single-Objective Results for Multi-objective Evolutionary Combinatorial Optimization. In Hamadi, Y., & Schoenauer, M. (Eds.), Learning and Intelligent Optimization (pp. 396–401). Lecture Notes in Computer Science. Springer Berlin Heidelberg.
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Loiacono, D., & Preuss, M. (2012). Computational intelligence in games. In Soule, T. (Ed.), Proceedings of the 14th annual conference companion on Genetic and evolutionary computation (pp. 1139–1140). ACM.
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Abschlussarbeit (Dissertation, Habilitation)

Grimme, C. (2012). Das Räuber-Beute-Modell für die mehrkriterielle Optimierung — Analyse und Anwendung. Dissertation at the Technische Universität Dortmund.
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2011

 

Aufsatz (Zeitschrift)

Fölling, A., Grimme, C., Lepping, J., & Papaspyrou, A. (2011). Connecting Community-Grids by Supporting Job Negotiation with Co-evolutionary Fuzzy-Systems. Soft Computing — A Fusion of Foundations, Methodologies and Applications, 15(12), 2375–2387.
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Naujoks, B., Trautmann, H., Wessing, S., & Weihs, C. (2011). Advanced concepts for multi-objective evolutionary optimization in aircraft industry. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225, 1081–1096.
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Aufsatz (Konferenz)

Bartz-Beielstein, T., & Preuss, M. (2011). Automatic and interactive tuning of algorithms. In Proceedings of the GECCO 2011, Dublin, Ireland, 1361–1379.
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Gerstl, K., Rudolph, G., Schütze, O., & Trautmann, H. (2011). Finding evenly spaced fronts for multiobjective control via averaging Hausdorff-measure. In Proceedings of 8th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 1–6.
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Grimme, C., Kemmerling, M., & Lepping, J. (2011). An expertise-guided multi-criteria approach to scheduling problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 47–48.
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Grimme, C., & Lepping, J. (2011). Combining basic heuristics for solving multi-objective scheduling problems. In IEEE Symposium on Computational Intelligence in Scheduling (SCIS), 9–16.
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Grimme, C., & Lepping, J. (2011). Integrating niching into the predator-prey model using epsilon-constraints. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 109–110.
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Hingston, P., & Preuss, M. (2011). Red teaming with coevolution. In Proceedings of the 2011 IEEE Congress of Evolutionary Computation, CEC 2011, New Orleans, LA, usa, 1155–1163.
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Kemmerling, M., Ackermann, N., & Preuss, M. (2011). Nested look-ahead evolutionary algorithm based planning for a believable diplomacy bot. In Proceedings of the Evo* 2011, Turin, Italy, 83–92.
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Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., & Rudolph, G. (2011). Exploratory landscape analysis. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, 829–836.
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Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., & Rudolph, G. (2011). Exploratory landscape analysis. In Proceedings of the GECCO 2011, Dublin, Ireland, 829–836.
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Preuss, M., Quadflieg, J., & Rudolph, G. (2011). TORCS sensor noise removal and multi-objective track selection for driving style adaptation. In Proceedings of the CIG 2011, Seoul, Korea, 337–344.
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Preuss, M., Stoean, C., & Stoean, R. (2011). Niching foundations: Basin identification on fixed-property generated landscapes. In Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, GECCO'11, Dublin, irl, 837–844.
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Quadflieg, J., Preuss, M., & Rudolph, G. (2011). Driving faster than a human player. In Proceedings of the EvoApplications/Evo* 2011, Turin, Italy, 143–152.
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Vatolkin, I., Preuß, M., & Rudolph, G. (2011). Multi-objective feature selection in music genre and style recognition tasks. In Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, GECCO'11, Dublin, irl, 411–418.
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Wagner, T., Trautmann, H., & Marti, L. (2011). A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms. In Takahashi, R., Deb, K., Wanner, E., & Greco, S. (Eds.), Evolutionary Multi-Criterion Optimization (pp. 16–30). Lecture Notes in Computer Science: Vol. 6576. Springer Berlin — Heidelberg.
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Wessing, S., Preuss, M., & Rudolph, G. (2011). When parameter tuning actually is parameter control. In Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, GECCO'11, Dublin, irl, 821–827.
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Buch (Monographie)

Terveer, I., & Terveer, S. (2011). Analysis-Brückenkurs für Wirtschaftswissenschaften (1st ed.). Konstanz, München: UVK-Lucius.
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Buch (Sammel-, Herausgeberband)

Di, C. C., Merelo, J., Cagnoni, S., Neri, F., Cotta, C., Preuss, M., Ebner, M., Richter, H., Ekárt, A., Togelius, J., Esparcia-Alcázar, A., & Yannakakis, G. (Eds.) (2011). Applications of Evolutionary Computation — EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Torino, Italy, April 27-29, 2011, Proceedings, Part I. Lecture Notes in Computer Science: Vol. 6624. Springer.
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2010

 

Aufsatz (Zeitschrift)

Azene, Y., Roy, R., Farrugia, D., Onisa, C., Mehnen, J., & Trautmann, H. (2010). Work roll cooling system design optimisation in presence of uncertainty and constrains. CIRP Journal of Manufacturing Science and Technology, 2, 290–298.
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Fölling, A., Grimme, C., Lepping, J., & Papaspyrou, A. (2010). Robust Load Delegation in Service Grid Environments. IEEE Transactions on Parallel and Distributed Systems, 21(9), 1304–1316.
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Loiacono, D., Lanzi, P., Togelius, J., Onieva, E., Pelta, D., Butz, M., Lönneker, T., Cardamone, L., Perez, D., Sáez, Y., Preuss, M., & Quadflieg, J. (2010). The 2009 simulated car racing championship. IEEE Transactions on Computational Intelligence and AI in Games, 2(2), 131–147.
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Stoean, C., Preuss, M., Stoean, R., & Dumitrescu, D. (2010). Multimodal optimization by means of a topological species conservation algorithm. IEEE Transactions on Evolutionary Computation, 14(6), 842–864.
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Wagner, T., & Trautmann, H. (2010). Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions. IEEE Transactions on Evolutionary Computation, 14(5), 688–701.
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Aufsatz (Konferenz)

Bartz-Beielstein, T., & Preuss, M. (2010). Tuning and experimental analysis in EC: What we still have wrong. In Proceedings of the GECCO 2010, Portland, Oregon, USA, 2625–2646.
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Beume, N., Naujoks, B., Preuss, M., Rudolph, G., & Wagner, T. (2010). Effects of 1-greedy S-metric-selection on innumerably large Pareto fronts. In Proceedings of the EMO 2009, Nantes, France, 21–35.
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Bischl, B., Mersmann, O., & Trautmann, H. (2010). Resampling Methods in Model Validation. In Bartz-Beielstein, T., Chiarandini, M., Paquete, L., & Preuss, M. (Eds.), Proceedings of the Workshop on Experimental Methods for the Assessment of Computational Systems (WEMACS 2010), Algorithm Engineering Report TR10-2-007. Department of Computer Science, TU Dortmund University.
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Bischl, B., Vatolkin, I., & Preuss, M. (2010). Selecting small audio feature sets in music classification by means of asymmetric mutation. In Proceedings of the PPSN 2010, Krakow, Poland, 314–323.
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Ding, J., Wessing, S., Trautmann, H., Mehnen, J., & Naujoks, B. (2010). Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing. In Teti, R. (Ed.), Proceedings of the 7th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '10). Capri, Italy: Copyright C.O.C. Com. org. Conv.
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Fölling, A., Grimme, C., Lepping, J., & Papaspyrou, A. (2010). The Gain of Resource Delegation in Distributed Computing Environments. In Schwiegelshohn, U., & Frachtenberg, E. (Eds.), Proceedings of the 15th Workshop on Job Scheduling Strategies for Parallel Processing (pp. 77–92). Lecture Notes in Computer Science. Atlanta (GA), United States: Springer.
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Grimme, C., Lepping, J., Moreno, P. J., & Papaspyrou, A. (2010). Applying P2P Strategies to Scheduling in Decentralized Grid Computing Infrastructures. In Proceedings of the Sixth International Workshop on Scheduling and Resource Management for Parallel and Distributed Systems, 295–302.
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Hingston, P., Preuss, M., & Spierling, D. (2010). RedTNet: A network model for strategy games. In Proceedings of the CEC 2010, Barcelona, Spain.
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Kemmerling, M., & Preuss, M. (2010). Automatic adaptation to generated content via car setup optimization in TORCS. In Proceedings of the CIG 2010, Copenhagen, Denmark, 131–138.
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Mersmann, O., Preuss, M., & Trautmann, H. (2010). Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In Schaefer, R., Cotta, C., Kolodziej, J., & Rudolph, G. (Eds.), 11th International Conference on Parallel Problem Solving from Nature — PPSN XI, Proceedings, Part I (pp. 73–82). Lecture Notes in Computer Science: Vol. 6238. Springer.
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Mersmann, O., Preuss, M., & Trautmann, H. (2010). Benchmarking evolutionary algorithms: Towards exploratory landscape analysis. In Proceedings of the PPSN 2010, Krakow, Poland, 73–82.
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Mersmann, O., Trautmann, H., Naujoks, B., & Weihs, C. (2010). On the Distribution of EMOA Hypervolumes. In Blum, C., & Battiti, R. (Eds.), Learning and Intelligent Optimization, 4th International Conference, LION 4, Venice, Italy (pp. 333–337). Lecture Notes in Computer Science: Vol. 6073. Springer.
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Mersmann, O., Trautmann, H., Naujoks, B., & Weihs, C. (2010). Benchmarking evolutionary multiobjective optimization algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 1–8.
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Mostaghim, S., Trautmann, H., & Mersmann, O. (2010). Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities. In Schaefer, R., Cotta, C., Kolodziej, J., & Rudolph, G. (Eds.), 11th International Conference on Parallel Problem Solving from Nature — PPSN XI, Proceedings, Part II (pp. 101–110). Lecture Notes in Computer Science: Vol. 6239. Springer.
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Preuss, M. (2010). Niching the CMA-ES via nearest-better clustering. In Proceedings of the GECCO 2010, Portland, OR, USA, 1711–1717.
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Preuss, M., Kausch, C., Bouvy, C., & Henrich, F. (2010). Decision space diversity can be essential for solving multiobjective real-world problems. In Proceedings of the MCDM 2008, Auckland, New Zealand, 367–377.
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Preuss, M., Rudolph, G., & Wessing, S. (2010). Tuning optimization algorithms for real-world problems by means of surrogate modeling. In Proceedings of the GECCO 2010, Portland, Oregon, USA, 401–408.
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Quadflieg, J., Preuss, M., Kramer, O., & Rudolph, G. (2010). Learning the track and planning ahead in a car racing controller. In Proceedings of the CIG 2010, Copenhagen, Denmark, 395–402.
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Shir, O., Preuss, M., Naujoks, B., & Emmerich, M. (2010). Enhancing decision space diversity in evolutionary multiobjective algorithms. In Proceedings of the EMO 2009, Nantes, France, 95–109.
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Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., & Yannakakis, G. (2010). Multiobjective exploration of the StarCraft map space. In Proceedings of the CIG 2010, Copenhagen, Denmark, 265–272.
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Togelius, J., Preuss, M., & Yannakakis, G. (2010). Towards multiobjective procedural map generation. In Proceedings of the FDG 2010, Monterey, California, USA.
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Voss, T., Trautmann, H., & Igel, C. (2010). New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization. In Schaefer, R., Cotta, C., Kolodziej, J., & Rudolph, G. (Eds.), 11th International Conference on Parallel Problem Solving from Nature — PPSN XI, Proceedings, Part II (pp. 260–269). Lecture Notes in Computer Science: Vol. 6239. Springer.
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Wagner, T., & Trautmann, H. (2010). Online convergence detection for evolutionary multi-objective algorithms revisited. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 1–8.
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Buch (Sammel-, Herausgeberband)

Bartz-Beielstein, T., Chiarandini, M., Paquete, L., & Preuss, M. (Eds.) (2010). Experimental methods for the analysis of optimization algorithms. Experimental Methods for the Analysis of Optimization Algorithms. Springer Berlin Heidelberg.
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Buchbeitrag (Sammel-, Herausgeberband)

Bartz-Beielstein, T., Preuss, M., Schmitt, K., & Schwefel, H. (2010). Model Optimization with Evolutionary Algorithms. In Lucas, K., & Roosen, P. (Eds.), Emergence, Analysis and Evolution of Structures---Concepts and Strategies Across Disciplines (pp. 47–62). Understanding Complex Systems: Vol. 2010. Springer.
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Bartz-Beielstein, T., Lasarczyk, C., & Preuss, M. (2010). The sequential parameter optimization toolbox. In Bartz-Beielstein, T., Thomas, B.-B., Chiarandini, M., Paquete, L., & Preuss, M. (Eds.), Experimental Methods for the Analysis of Optimization Algorithms (pp. 337–362). Springer Berlin Heidelberg.
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Bartz-Beielstein, T., & Preuss, M. (2010). The future of experimental research. In Bartz-Beielstein, T., Chiarandini, M., Paquete, L., & Preuss, M. (Eds.), Experimental Methods for the Analysis of Optimization Algorithms (pp. 17–49). Springer Berlin Heidelberg.
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2009

 

Aufsatz (Zeitschrift)

Fölling, A., Grimme, C., Lepping, J., Papaspyrou, A., & Schwiegelshohn, U. (2009). Competitive Co-evolutionary Learning of Fuzzy Systems for Job Exchange in Computational Grids. Evolutionary Computation, 17(4), 545–560.
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Grimme, C., & Papaspyrou, A. (2009). Cooperative Negotiation and Scheduling of Scientific Workflows in the Collaborative Climate Community Data and Processing Grid. Future Generation Computer Systems, 25, 301–307.
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Plantikow, S., Peter, K., Högqvist, M., Grimme, C., & Papaspyrou, A. (2009). Generalizing the Data Management of Three Community Grids. Future Generation Computer Systems, 25, 281–289.
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Stoean, C., Preuss, M., & Stoean, R. (2009). Species separation by a clustering mean towards multimodal function optimization. An. Univ. Craiova, Ser. Mat. Inf. 36(2), 53–62.
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Stoean, R., Preuss, M., Stoean, C., El-Darzi, E., & Dumitrescu, D. (2009). Support vector machine learning with an evolutionary engine. Journal of the Operational Research Society, 60(8), 1116–1122.
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Trautmann, H., & Mehnen, J. (2009). Statistical Methods for Improving Multi-objective Evolutionary Optimisation. International Journal of Computational Intelligence Research, 5(2), 72–78.
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Trautmann, H., & Mehnen, J. (2009). Preference-Based Pareto-Optimization in Certain and Noisy Environments. Engineering Optimization, 41, 23–38.
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Trautmann, H., Wagner, T., Naujoks, B., Preuss, M., & Mehnen, J. (2009). Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms. Evolutionary Computation, Special Issue: Twelve Years of EC Research in Dortmund, 17(4), 493–509.
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Aufsatz (Konferenz)

Fölling, A., Grimme, C., Lepping, J., & Papaspyrou, A. (2009). Decentralized Grid Scheduling with Evolutionary Fuzzy Systems. In Frachtenberg, E., & Schwiegelshohn, U. (Eds.), Proceedings of the 14th Job Scheduling Strategies for Parallel Processing (pp. 16–36). Lecture Notes in Computer Science: Vol. 5798. Springer.
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Fölling, A., Grimme, C., Lepping, J., & Papaspyrou, A. (2009). Co-evolving Fuzzy Rule Sets for Job Exchange in Computational Grids. In Proceedings of the International Conference on Fuzzy Systems, Jeju Island, Korea, 1683–1688.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2009). Adapting to the Habitat: On the Integration of Local Search into the Predator-Prey Model. In Proceedings of the Fifth International Conference on Evolutionary Multi-Criterion Optimization (EMO), 510–524.
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Kemmerling, M., Ackermann, N., Beume, N., Preuss, M., Uellenbeck, S., & Walz, W. (2009). Is human-like and well playing contradictory for diplomacy hots?. In Proceedings of the CIG 2009, Milano, Italy, 209–216.
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Naujoks, B., & Trautmann, H. (2009). Online Convergence Detection for Multiobjective Aerodynamic Applications. In Tyrrell, A. (Ed.), Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway (pp. 332–339). IEEE Press.
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Preuss, M. (2009). Adaptability of algorithms for real-valued optimization. In Proceedings of the EvoApplications/Evo* 2009, Tübingen, Germany, 665–674.
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Rudolph, G., & Preuss, M. (2009). A multiobjective approach for finding equivalent inverse images of pareto-optimal objective vectors. In Proceedings of the MCDM 2009, Orlando, FL, USA, 74–79.
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Rudolph, G., Preuss, M., & Quadflieg, J. (2009). Double-layered Surrogate Modeling for Tuning Metaheuristics. In Proceedings of the ENBIS/EMSE Conference "Design and Analysis of Computer Experiments", Saint-Etienne (France).
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T., W. H. T., & Naujoks, B. (2009). OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing. In Fonseca, C., & Gandibleux, X. (Eds.), Evolutionary Multi-Criterion Optimization (EMO 2009), Lecture Notes in Computer Science (LNCS) 5467 (pp. 198–215). Springer, Berlin.
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Trautmann, H., Mehnen, J., & Naujoks, B. (2009). Pareto-Dominance in Noisy Environments. In Tyrrell, A. (Ed.), Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway (pp. 3119–3126). IEEE Press.
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Buch (Sammel-, Herausgeberband)

Giacobini, M., Brabazon, A., Cagnoni, S., Caro, G., Ekárt, A., Esparcia-Alćazar, A., Farooq, M., Fink, A., Machado, P., McCormack, J., O'Neill, M., Neri, F., Preuss, M., Rothlauf, F., Tarantino, E., & Yang, S. (Eds.) (2009). Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. null.
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Buchbeitrag (Sammel-, Herausgeberband)

Shir, O., Preuss, M., Naujoks, B., & Emmerich, M. (2009). Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms. In Ehrgott, M., Fonseca, C., Gandibleux, X., Hao, J., & Sevaux, M. (Eds.), Evolutionary Multi-Criterion Optimization (pp. 95–109). Lecture Notes in Computer Science: Vol. 5467. Springer.
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Stoean, R., Preuss, M., Stoean, C., El-Darzi, E., & Dumitrescu, D. (2009). An evolutionary approximation for the coefficients of decision functions within a support vector machine learning strategy. In Hassanien, A. E., Abraham, A., Vasilakos, A. V., & Pedrycz, W. (Eds.), Foundations of Computational Intelligence, Volume 1 (pp. 83–114).
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2008

 

Aufsatz (Zeitschrift)

Henrich, F., Bouvy, C., Kausch, C., Lucas, K., Preuß, M., Rudolph, G., & Roosen, P. (2008). Economic optimization of non-sharp separation sequences by means of evolutionary algorithms. Computers and Chemical Engineering, 32(7), 1411–1432.
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Preuss, M., & Beume, N. (2008). Learning from failures in evolutionary computation — GECCO-2009: events reports. SIGEVOlution, 3(4), 16–18.
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Aufsatz (Konferenz)

Bartz-Beielstein, T., & Preuss, M. (2008). Experimental research in evolutionary computation. In Proceedings of the GECCO 2008, Atlanta, GA, USA, 2517–2534.
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Beume, N., Danielsiek, H., Eichhorn, C., Naujoks, B., Preuss, M., Stiller, K., & Wessing, S. (2008). Measuring flow as concept for detecting game fun in the pac-man game. In Proceedings of the CEC 2008, Hongkong, China, 3448–3455.
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Beume, N., Hein, T., Naujoks, B., Neugebauer, G., Piatkowski, N., Preuss, M., Stüer, R., & Thom, A. (2008). To Model or Not to Model: Controlling Pac-Man Ghosts Without Incorporating Global Knowledge. In Proceedings of the 2008 Congress on Evolutionary Computation (CEC'08), Hong Kong, China, 3464–3471.
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Beume, N., Hein, T., Naujoks, B., Piatkowski, N., Preuss, M., & Wessing, S. (2008). Intelligent anti-grouping in real-time strategy games. In Proceedings of the CIG 2008, Perth, Australia, 63–70.
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Bouvy, C., Kausch, C., Preuss, M., & Henrich, F. (2008). On the potential of multi-objective optimisation in the design of sustainable energy systems. In Ehrgott, M., & others, (Eds.), MCDM for Sustainable Energy and Transportation Systems. Springer.
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Danielsiek, H., Stüer, R., Thom, A., Beume, N., Naujoks, B., & Preuss, M. (2008). Intelligent moving of groups in real-time strategy games. In Proceedings of the CIG 2008, Perth, Australia, 71–78.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2008). Prospects of Collaboration between Compute Providers by means of Job Interchange. In Frachtenberg, E., & Schwiegelshohn, U. (Eds.), Proceedings of the 13th Job Scheduling Strategies for Parallel Processing (pp. 132–151). Lecture Notes in Computer Science: Vol. 4942. Springer.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2008). Benefits of Job Exchange Between Autonomous Sites in Decentralized Computational Grids. In Priol, T., Lefevre, L., & Buyya, R. (Eds.), Proccedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid) (pp. 25–32). IEEE Computer Society.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2008). The Parallel Predator-Prey Model: A Step Towards Practical Application. In Rudolph, G., & others, (Eds.), Proceedings of the 10th International Conference on Parallel Problem Solving From Nature (PPSN X) (pp. 681–690). Lecture Notes in Computer Science: Vol. 5199. Springer.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2008). Discovering Performance Bounds for Grid Scheduling by using Evolutionary Multiobjective Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 1491–1498.
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Jägersküpper, J., & Preuss, M. (2008). Aiming for a theoretically tractable CSA variant by means of empirical investigations. In Proceedings of the GECCO 2008, Atlanta, GA, USA, 503–510.
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Jägersküpper, J., & Preuss, M. (2008). Empirical investigation of simplified step-size control in metaheuristics with a view to theory. In Proceedings of the WEA 2008, Provincetown, MA, USA, 263–274.
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Kausch, C., Preuss, M., & Henrich, F. (2008). Multi-criteria Pareto optimised planning method for distillation plants with non-sharp splits. In Proceedings of the 21st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2008, pol, 773–780.
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Mehnen, J., & Trautmann, H. (2008). Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing. In Teti, R. (Ed.), Proceedings of the 6th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '08). Naples, Italy: Copyright C.O.C. Com. org. Conv.
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Rudolph, G., & Preuss, M. (2008). Ein Evolutionsverfahren zur Approximation äquivalenter Urbilder von Pareto-optimalen Zielvektoren. In Mikut, R., & Reischl, M. (Eds.), Proc. 18th GMA Workshop Computational Intelligence (pp. 30–39). Universitätsverlag, Karlsruhe.
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Stoean, C., Preuss, M., Stoean, R., & Dumitrescu, D. (2008). EA-powered basin number estimation by means of preservation and exploration. In Proceedings of the PPSN 2008, Dortmund, Germany, 569–578.
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Stoean, C., Stoean, R., & Preuss, M. (2008). Approximating the Number of Attraction Basins of a Function by Means of Clustering and Evolutionary Algorithms. In Proceedings of the AIDC 2008, Craiova, Romania, 171–180.
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Trautmann, H., Ligges, U., Mehnen, J., & Preuss, M. (2008). A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing. In Rudolph, G., & others, (Eds.), Parallel Problem Solving from Nature (PPSN) (pp. 825–836). Springer, Berlin.
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Trautmann, H., Ligges, U., Mehnen, J., & Preuss, M. (2008). A convergence criterion for multiobjective evolutionary algorithms based on systematic statistical testing. In Proceedings of the PPSN 2008, Dortmund, Germany, 825–836.
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2007

 

Aufsatz (Zeitschrift)

Stoean, C., Dumitrescu, D., Preuss, M., & Stoean, R. (2007). Cooperative coevolution as a paradigm for classification. Journal of Universal Computer Science, 13(7), 1097–1108.
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Stoean, R., Dumitrescu, D., Preuss, M., & Stoean, C. (2007). Evolutionary support vector machines for classification with multiple outcomes. Journal of Universal Computer Science, 13(7), 1109–1121.
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Stoean, R., Stoean, C., Preuss, M., & Dumitrescu, D. (2007). Evolutionary Detection of Separating Hyperplanes in E-mail Classification. Acta Cibiniensis, LV, 41–46.
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Aufsatz (Konferenz)

Bartz-Beielstein, T., & Preuss, M. (2007). Experimental research in evolutionary computation. In Proceedings of the GECCO 2007, London, UK, 3001–3020.
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Chimani, M., Kandyba, M., & Preuss, M. (2007). Hybrid numerical optimization for combinatorial network problems. In Proceedings of the Hybrid Metaheuristics 2007, Dortmund, Germany, 185–200.
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Freitag, S., Grimme, C., Papaspyrou, A., & Schley, L. (2007). On the Applicability of OGSA-BES to D-Grid Community Scheduling Systems. In Proceedings of the German E-Science Conference (GES).
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Grimme, C., Langhammer, T., Papaspyrou, A., & Schintke, F. (2007). Negotiation-based Choreography of Data-intensive Applications in the C3Grid Project. In Proceedings of the German E-Science Conference (GES).
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Grimme, C., & Lepping, J. (2007). Designing Multi-Objective Variation Operators Using a Predator-Prey Approach. In Proceedings of the Fourth International Conference on Evolutionary Multi-Criterion Optimization, 21–35.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2007). Exploring the Behavior of Building Blocks for Multi-Objective Variation Operator Design using Predator-Prey Dynamics. In Thierens, D., & others, (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 805–812). London: ACM Press.
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Grimme, C., Lepping, J., & Papaspyrou, A. (2007). Identifying Job Migration Characteristics in Decentralized Grid Scheduling Scenarios. In Zheng, S. (Ed.), Proceedings of the 19th International Conference on Parallel and Distributed Computing and Systems (PDCS) (pp. 124–129). Cambridge (MA), USA: ACTA Press.
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Mehnen, J., Trautmann, H., & Tiwari, A. (2007). Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes. In CEC 2007, IEEE Congress on Evolutionary Computation, 2687–2694.
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Preuss, M., & Naujoks, B. (2007). Evolutionäre mehrkriterielle Optimierung bei Anwendungen mit nichtzusammenhängenden Pareto-Mengen. In Mikut, R., & Reischl, M. (Eds.), Proc. 17th Workshop Computational Intelligence (pp. 165–176). Universitätsverlag, Karlsruhe.
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Preuss, M., Rudolph, G., & Tumakaka, F. (2007). Solving multimodal problems via multiobjective techniques with application to phase equilibrium detection. In Proceedings of the CEC 2007, Singapore, 2703–2710.
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Rudolph, G., Naujoks, B., & Preuss, M. (2007). Capabilities of EMOA to detect and preserve equivalent pareto subsets. In Proceedings of the EMO 2007, Matsushima, Japan, 36–50.
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Rudolph, G., & Preuss, M. (2007). Ein mehrkriterielles Evolutionsverfahren zur Bestimmung des Phasengleichgewichts von gemischten Flüssigkeiten. In Mikut, R., & Reischl, M. (Eds.), Proc. 17th Workshop Computational Intelligence (pp. 177–185). Universitätsverlag, Karlsruhe.
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Stoean, C., Dumitrescu, D., Preuss, M., & Stoean, R. (2007). Cooperative evolution of rules for classification. In Proceedings of the SYNASC 2006, Timisoara, Romania, 317–322.
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Stoean, C., Stoean, R., Preuss, M., & Dumitrescu, D. (2007). Competitive Coevolution for Classification. In Proceedings of the 7th International Conference on Artificial Intelligence and Digital Communications, Craiova, Romania, 28–39.
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Stoean, C., Preuss, M., Stoean, R., & Dumitrescu, D. (2007). Disburdening the species conservation evolutionary algorithm of arguing with radii. In Proceedings of the 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007, London, gbr, 1420–1427.
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Stoean, R., Preuss, M., Dumitrescu, D., & Stoean, C. (2007). Evolutionary support vector regression machines. In Proceedings of the SYNASC 2006, Timisoara, Romania, 330–335.
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Stoean, R., Preuss, M., Stoean, C., & Dumitrescu, D. (2007). Concerning the potential of evolutionary support vector machines. In Proceedings of the CEC 2007, Singapore, 1436–1443.
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Weihs, C., & Trautmann, H. (2007). Parallel Universes: Multi-Criteria Optimization. In Berthold, M., Morik, K., & Siebes, A. (Eds.), Dagstuhl Seminar Proceedings 07181, Parallel Universes and Local Patterns. Schloss Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum f�r Informatik (IBFI).
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Buchbeitrag (Sammel-, Herausgeberband)

Preuss, M., & Bartz-Beielstein, T. (2007). Sequential parameter optimization applied to self-adaptation for binary-coded evolutionary algorithms. In Lobo, F., Lima, C., & Michalewicz, Z. (Eds.), Parameter Setting in Evolutionary Algorithms (pp. 91–119). Studies in Computational Intelligence: Vol. 54/2007.
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Bericht (Arbeits-, Forschungs-, Jahresbericht)

Chiarandini, M., Paquete, L., Preuss, M., & Ridge, E. (2007). Experiments on metaheuristics: Methodological overview and open issues. In The Danish Mathematical Society: Vol. DMF-2007-03-003.
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Grimme, C., Lepping, J., Papaspyrou, A., Wieder, P., Yahyapour, R., Oleksiak, A., Wäldrich, O., & Ziegler, W. (2007). Towards a standards-based Grid Scheduling Architecture. Institute on Resource Management and Scheduling.
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Huang, V., Qin, A., Deb, K., Zitzler, E., Suganthan, P., Liang, J., Preuss, M., & Huband, S. (2007). Problem Definitions for Performance Assessment of Multi-objective Optimization Algorithms. In Nanyang Technological University, Singapore.
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Preuss, M. (2007). Reporting on Experiments in Evolutionary Computation. In University of Dortmund, SFB 531: Vol. CI-221/07.
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2006

 

Aufsatz (Zeitschrift)

Stoean, R., Stoean, C., Preuss, M., & Dumitrescu, D. (2006). Forecasting Soybean Diseases from Symptoms by Means of Evolutionary Support Vector Machines. Phytologia Balcanica, 12(3), 345–350.
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Stoean, R., Stoean, C., Preuss, M., & Dumitrescu, D. (2006). Evolutionary Multi-class Support Vector Machines for Classification. International Journal of Computers, Communications — Control, 1(Supplementary Issue, International Conference on Computers and Communications — ICCC 2006), 423–428.
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Trautmann, H., & Weihs, C. (2006). On the Distribution of the Desirability Index using Harrington's Desirability Function. Metrika, 63(2), 207–213.
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Aufsatz (Konferenz)

Bartz-Beielstein, T., & Preuss, M. (2006). Considerations of Budget Allocation for Sequential Parameter Optimization (SPO). In Proceedings of the PPSN 2006 (EMAA Workshop), Reykjavik, Iceland, 35–40.
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Bartz-Beielstein, T., & Preuss, M. (2006). Moderne Methoden zur experimentellen Analyse evolutionärer Verfahren. In Mikut, R., & Reischl, M. (Eds.), Proc. 16th Workshop Computational Intelligence (pp. 25–32). Universitätsverlag, Karlsruhe.
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Bartz-Beielstein, T., Preuss, M., & Rudolph, G. (2006). Investigation of one-go evolution strategy/quasi-newton hybridizations. In Proceedings of the Hybrid Metaheuristics 2006, Gran Canaria, Spain, 178–191.
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Giacobini, M., Preuss, M., & Tomassini, M. (2006). Effects of scale-free and small-world topologies on binary coded self-adaptive CEA. In Proceedings of the EvoCop/Evo* 2006, Budapest, Hungary, 86–98.
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Grimme, C., & Schmitt, K. (2006). Inside a Predator-Prey Model for Multi-Objective Optimization: A Second Study. In H., G. B., & others, (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (pp. 707–714). New York: ACM Press.
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Mehnen, J., & Trautmann, H. (2006). Integration of Expert's Preferences in Pareto Optimization by Desirability Function Techniques. In Teti, R. (Ed.), CIRP ICME '06) — Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (pp. 293–298). Ischia, Italy: C.O.C. Com. org. Conv. CIRP ICME '06.
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Preuss, M. (2006). Niching prospects. In Proceedings of the BIOMA 2006, Ljubljana, Slovenia, 25–34.
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Preuss, M., Naujoks, B., & Rudolph, G. (2006). Pareto set and EMOA behavior for simple multimodal multiobjective functions. In Proceedings of the PPSN 2006, Reykjavik, Iceland, 513–522.
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Stoean, C., Dumitrescu, D., Preuss, M., & Stoean, R. (2006). Cooperative Coevolution for Classification. In Proceedings of the Bio-Inspired Computing: Theory and Applications (BIC-TA 2006), Wuhan, China, 289–298.
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Stoean, C., Stoean, R., Preuss, M., & Dumitrescu, D. (2006). Spam Filtering by Means of Cooperative Coevolution. In Teodorescu, H. (Ed.), 4th European Conference on Intelligent Systems and Technologies, ECIT 2006 (pp. 157–159). Performantica Press.
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Stoean, R., Dumitrescu, D., Preuss, M., & Stoean, C. (2006). Different Techniques of Multi-class Evolutionary Support Vector Machines. In Proceedings of the Bio-Inspired Computing: Theory and Applications (BIC-TA 2006), Wuhan, China, 299–306.
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Stoean, R., Stoean, C., Preuss, M., & Dumitrescu, D. (2006). Evolutionary Support Vector Machines for Spam Filtering. In Proceedings of the RoEduNet IEEE International Conference, Iasi, Romania, 261–266.
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Stoean, R., Stoean, C., Preuss, M., El-Darzi, E., & Dumitrescu, D. (2006). Evolutionary support vector machines for diabetes mellitus diagnosis. In Proceedings of the IEEE Intelligent Systems 2006, London, UK, 182–187.
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Buchbeitrag (Sammel-, Herausgeberband)

Paechter, B., Willies, J., & Preuss, M. (2006). Prologue. In Bartz-Beielstein, T., Jankord, G., Naujoks, B., Rudolph, G., & Schmitt, K. (Eds.), Hans-Paul Schwefel — Festschrift (pp. 83–84). University of Dortmund, Chair of Systems Analysis.
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2005

 

Aufsatz (Konferenz)

Bartz-Beielstein, T., Lasarczyk, C., & Preuss, M. (2005). Sequential parameter optimization. In Proceedings of the CEC 2005, Edinburgh, UK, 773–780.
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Preuss, M., Schönemann, L., & Emmerich, M. (2005). Counteracting genetic drift and disruptive recombination in (μ, +λ)-EA on multimodal fitness landscapes. In Proceedings of the GECCO 2005, Washington, DC, USA, 865–872.
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Stoean, C., Preuss, M., Gorunescu, R., & Dumitrescu, D. (2005). Elitist generational genetic chromodynamics — A new radii-based evolutionary algorithm for multimodal optimization. In Proceedings of the CEC 2005, Edinburgh, UK, 1839–1846.
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Stoean, C., Stoean, R., Preuss, M., & Dumitrescu, D. (2005). Diabetes diagnosis through the means of a multimodal evolutionary algorithm. In B.~McKay, o. (Ed.), Proc.~1st East European Conference on Health Care Modelling and Computation (HCMC'2005), Craiova, Romania (pp. 277–289). Craiova: Medical University Press.
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Buchbeitrag (Sammel-, Herausgeberband)

Bartz--Beielstein, T., Preuss, M., & Markon, S. (2005). Validation and Optimization of an Elevator Simulation Model with Modern Search Heuristics. In Ibaraki, T., Nonobe, K., & Yagiura, M. (Eds.), Metaheuristics — Progress as Real Problem Solvers (pp. 109–128). Kluwer Academic Publishers.
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Bericht (Arbeits-, Forschungs-, Jahresbericht)

Grimme, C. (2005). Räuber-Beute-Systeme für die mehrkriterielle Optimierung. Technische Universität Dortmund.
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2004

 

Aufsatz (Zeitschrift)

Schönemann, L., Emmerich, M., & Preuss, M. (2004). On the extinction of evolutionary algorithm subpopulations on multimodal landscapes. Informatica (Sl), 28(4), 345–351.
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Stoean, C., Gorunescu, R., Preuss, M., & Dumitrescu, D. (2004). An Evolutionary Learning Classifier System Applied to Text Categorization. Annal of West University of Timisoara, Mathematics and Computer Science Series, XLII(special issue 1), 265–278.
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Aufsatz (Konferenz)

Preuss, M., & Lasarczyk, C. (2004). On the importance of information speed in structured populations. In Proceedings of the PPSN 2004, Birmingham, UK, 91–100.
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Schönemann, L., Emmerich, M., & Preuss, M. (2004). On the extinction of sub-populations on multimodal landscapes. In Proceedings of the BIOMA 2004, Ljubljana, Slovenia, 31–40.
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Stoean, C., Gorunescu, R., Preuss, M., & Dumitrescu, D. (2004). Evolutionary Detection of Rules for Text Categorization. Application to Spam Filtering. In Teodorescu, H. (Ed.), Intelligent Systems — Fuzzy Systems, Neural Networks, Genetic Algorithms, Heuristics and Nonlinear Systems — Selected papers of the Third European Conference on Intelligent Systems and Technologies — ECIT'2004 (pp. 87–95). Iasi, Romania: Performantica Press.
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Stoean, C., Gorunescu, R., Preuss, M., & Dumitrescu, D. (2004). An Evolutionary Learning Spam Filter System. In Petcu, D., Zaharie, D., Negru, V., & Jebelean, T. (Eds.), Proceedings 6th International Symposium, SYNASC04 — Symbolic and Numeric Algorithms for Scientific Computing (pp. 512–522). Timisoara, Romania: Mirton Publishing House.
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Abschlussarbeit (Dissertation, Habilitation)

Trautmann, H. (2004). Qualitätskontrolle in der Industrie anhand von Kontrollkarten für Wünschbarkeitsindizes — Anwendungsfeld Lagerverwaltung. at the Universität Dortmund.
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2003

 

Aufsatz (Konferenz)

Beielstein, T., Markon, S., & Preuss, M. (2003). A parallel approach to elevator optimization based on soft computing. In Proceedings of the 5th Metaheuristics Int'l Conf. (MIC'03), Kyoto, Japan, 07/1-07/11 (CD-ROM).
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Beielstein, T., Markon, S., & Preuss, M. (2003). Algorithm based validation of a simplified elevator group controller model. In Proceedings of the 5th Metaheuristics Int'l Conf.~(MIC'03), Kyoto, Japan, 06/1-06/13 (CD-ROM).
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2002

 

Aufsatz (Konferenz)

Arenas, M., Collet, P., Eiben, A., Jelasity, M., Merelo, J., Paechter, B., Preuss, M., & Schoenauer, M. (2002). A framework for distributed evolutionary algorithms. In J.~J.~Merelo~Guervós, P., H.--G.~Beyer, J.--V., & H.--P.~Schwefel, (Eds.), Parallel Problem Solving from Nature — PPSN VII, Proc.~Seventh Intl. Conf. (pp. 665–675). Lecture Notes in Computer Science: Vol. 2439. Springer.
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Jelasity, M., & Preuss, M. (2002). On obtaining global information in a peer-to-peer fully distributed environment. In B.~Monien, R. (Ed.), Euro-Par 2002 Parallel Processing, Proc.~Eighth Int'l Conf., Paderborn, August~2002 (pp. 573–577). Lecture Notes in Computer Science: Vol. 2400. Springer.
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Jelasity, M., Preuss, M., & Eiben, A. (2002). Operator learning for a problem class in a distributed peer-to-peer environment. In J.~J.~Merelo~Guervós, P., H.--G.~Beyer, J.--V., & H.--P.~Schwefel, (Eds.), Parallel Problem Solving from Nature — PPSN VII, Proc.~Seventh Int'l Conf., Granada, September~2002 (pp. 172–183). Lecture Notes in Computer Science: Vol. 2439. Springer.
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Jelasity, M., Preuss, M., & Paechter, B. (2002). A scalable and robust framework for distributed applications. In D.~B.~Fogel, M.-S., X.~Yao, G., H.~Iba, P., & M.~Shackleton, (Eds.), Proc.~2002 Congress on Evolutionary Computation (CEC'02) within Third IEEE World Congress on Computational Intelligence (WCCI'02) (pp. 1540–1545). IEEE Press.
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Jelasity, M., Preuss, M., Steen, M., & Paechter, B. (2002). Maintaining connectivity in a scalable and robust distributed environment. In H.~E.~Bal, K.--P., & A.~Reinefeld, (Eds.), Proc.~Second IEEE Int'l Symposium on Cluster Computing and the Grid (CCGrid'02) (pp. 389–394). {IEEE} Computer Society.
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Bericht (Arbeits-, Forschungs-, Jahresbericht)

Terveer, I. (2002). Die asymptotische Verteilung der Spannweite bei Zufallsgrößen mit paarweise identischer Korrelation. In Becker, J., Grob, H.-L., Klein, S., Kuchen, H., Müller-Funk, U., & Vossen, G. (Eds.), Arbeitsberichte des Instituts für Wirtschaftsinformatik: Vol. 74. Münster: Institut für Wirtschaftsinformatik, WWU Münster.
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1998

 

Aufsatz (Konferenz)

Bäck, T., Eiben, A., Graaf, J., Preuss, M., Schippers, A., & Taale, H. (1998). Optimizing traffic light controllers with evolutionary algorithms. In Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, 1730–1734.
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1996

 

Aufsatz (Zeitschrift)

Terveer, I. (1996). Bayesian Sequantially Planned Test of Simple Hypotheses: Contractivity and an Iterative Approach. Sequential Analysis, 15(2), 91–102.
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1995

 

Aufsatz (Zeitschrift)

Terveer, I. (1995). Cost-Optimal Multistage Sampling Plans in Statistical Quality Control. Zeitschrift für Operations Research, 41(1), 359–380.
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