• 2019

    Article in Journal

    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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    Conference Paper

    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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  • 2018

    Article in Journal

    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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    Conference Paper

    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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  • 2017

    Article in Journal

    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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    Conference Paper

    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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    Report

    Grimme, C., Frischlich, L., Preuß, M., Assenmacher, D., Adam, L., 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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  • 2016

    Article in Journal

    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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    Conference Paper

    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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  • 2015

    Article in Journal

    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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    Conference Paper

    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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    Book (Monography)

    Preuss, M. (2015). Multimodal Optimization by Means of Evolutionary Algorithms. Natural Computing Series. Springer.
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    Chapter in Book

    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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  • 2014

    Article in Journal

    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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    Conference Paper

    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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    Chapter in Book

    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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  • 2013

    Article in Journal

    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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    Conference Paper

    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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    Chapter in Book

    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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  • 2012

    Article in Journal

    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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    Conference Paper

    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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    Collection (Editor)

    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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    Chapter in Book

    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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  • 2011

    Conference Paper

    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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    Collection (Editor)

    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

    Article in Journal

    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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    Conference Paper

    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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    Collection (Editor)

    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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    Chapter in Book

    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

    Article in Journal

    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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    Conference Paper

    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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    Collection (Editor)

    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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    Chapter in Book

    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

    Article in Journal

    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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    Conference Paper

    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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  • 2007

    Article in Journal

    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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    Conference Paper

    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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    Chapter in Book

    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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    Report

    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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  • 2006

    Article in Journal

    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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    Conference Paper

    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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    Chapter in Book

    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

    Conference Paper

    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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    Chapter in Book

    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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  • 2004

    Article in Journal

    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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    Conference Paper

    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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  • 2003

    Conference Paper

    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

    Conference Paper

    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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  • 1998

    Conference Paper

    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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