Former Research Assistant
Data Science: Statistics and Optimization
Leonardo-Campus 3
48149
Münster
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Leonardo-Campus 3
48149
Münster
Rothmeier, K., Pflanzl, N., Hüllmann, J. A., & Preuss, M. (2020). Prediction of Player Churn and Disengagement Based on User Activity Data of a Freemium Online Strategy Game. IEEE Transactions on Games, 2020.
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Clever, L., Assenmacher, D., Müller, K., Seiler, M. V., Riehle, D. M., Preuss, M., & Grimme, C. (2020). FakeYou! — A Gamified Approach for Building and Evaluating Resilience Against Fake News. In Proceedings of the 2nd Multidisciplinary International Symposium on Disinformation in Open Online Media, Leiden, Netherlands.
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Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ), 27(4), 577–609.
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Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., & Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global
Optima in Multimodal Multi-Objective Optimization. In Proceedings of the International Global Optimization Workshop (LeGO 2018), Leiden, The Netherlands, 020052-1-020052-4.
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Segler, M., Preuss, M., & Waller, M. (2018). Planning Chemical Syntheses with Deep Neural Networks and Symbolic AI. Nature, 555, 604–610.
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Pappa, G. L., Emmerich, M. T., Bazzan, A., Browne, W., Deb, K., Doerr, C., Ðurasević, M., Epitropakis, M. G., Haraldsson, S. O., Jakobovic, D., Kerschke, P., Krawiec, K., Lehre, P. K., Li, X., Lissovoi, A., Malo, P., Martí, L., Mei, Y., Merelo, J. J., Miller, J. F., Moraglio, A., Nebro, A. J., Nguyen, S., Ochoa, G., Oliveto, P., Picek, S., Pillay, N., Preuss, M., Schoenauer, M., Senkerik, R., Sinha, A., Shir, O., Sudholt, D., Whitley, D., Wineberg, M., Woodward, J., & Zhang, M. (2018). Tutorials at PPSN 2018. In Auger, A., Fonseca, C. M., Lourenço, N., Machado, P., Paquete, L., & Whitley, D. (Eds.), Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN XV) (pp. 477–489). Cham: Springer International Publishing.
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Preuss, M., Pfeiffer, T., Volz, V., & Pflanzl, N. (2018). Integrated Balancing of an RTS Game: Case Study and Toolbox Refinement. In Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG, Maastricht. (Accepted)
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Purshouse, R., Zarges, C., Cussat-Blanc, S., Epitropakis, M. G., Gallagher, M., Jansen, T., Kerschke, P., Li, X., Lobo, F. G., Miller, J., Oliveto, P. S., Preuss, M., Squillero, G., Tonda, A., Wagner, M., Weise, T., Wilson, D., Wróbel, B., & Zamuda, A. (2018). Workshops at PPSN 2018. In Auger, A., Fonseca, C. M., Lourenço, N., Machado, P., Paquete, L., & Whitley, D. (Eds.), Proceedings of International Conference on Parallel Problem Solving from Nature (PPSN XV) (pp. 490–497). Cham: Springer International Publishing.
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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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Grimme, C., Preuss, M., Adam, L., & Trautmann, H. (2017). Social Bots: Human-Like by Means of Human Control?. Big Data, 5(4), 279–293.
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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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Wessing, S., & Preuss, M. (2017). The true destination of EGO is multi-local optimization. In Proceedings of the 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Arequipa, Peru.
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Grimme, C., Frischlich, L., Preuß, M., Assenmacher, D., Adam, L., Lütke-Stokdiek, J. F. H., Trautmann, H., & Quandt, T. (2017). Projekt 345: Validierung der automatisierten Erkennung von Social Bots. Bundesamt für Sicherheit in der Informationstechnik. (Accepted)
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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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Wessing, S., & Preuss, M. (2016). On multiobjective selection for multimodal optimization. Computational Optimization and Applications, 63(3), 875–902.
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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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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., 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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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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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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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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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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Preuss, M. (2015). Multimodal Optimization by Means of Evolutionary Algorithms. Natural Computing Series. Springer.
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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 Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, GECCO'12, Philadelphia, PA, usa, 313–320.
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Jordan, A., Scheftelowitsch, D., Lahni, J., Hartwecker, J., Kuchem, M., Walter-Huber, M., Vortmeier, N., Delbrügger, T., Güler, Ü., Vatolkin, 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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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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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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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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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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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 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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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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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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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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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., 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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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 Proceedings of the PPSN 2010, Krakow, Poland, 73–82.
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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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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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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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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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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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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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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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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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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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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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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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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 Proceedings of the PPSN 2008, Dortmund, Germany, 825–836.
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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