• 2019

    Aufsatz (Zeitschrift)

    Carnein, M., & Trautmann, H. (2019). Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. Business and Information Systems Engineering (BISE), 2019. (Accepted)
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    Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ), 27(1), 3–45.
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    Kerschke, P., & Trautmann, H. (2019). Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation (ECJ), 27(1), 99–127.
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    Aufsatz (Konferenz)

    Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In Battiti, R., Brunato, M., Kotsireas, I., & Pardalos, P. (Eds.), Learning and Intelligent Optimization (pp. 215–219). Lecture Notes in Computer Science: Vol. 11353. Cham: Springer.
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    Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm. In Deb, K., Goodman, E., Coello, C. C. A., Klamroth, K., Miettinen, K., Mostaghim, S., & Reed, P. (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp. 516–528). Lecture Notes in Computer Science: Vol. 11411. Cham: Springer International Publishing.
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    Carnein, M., & Trautmann, H. (2019). Customer Segmentation Based on Transactional Data Using Stream Clustering. In Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '19), Macau, China. (Accepted)
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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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    Grimme, C., Kerschke, P., & Trautmann, H. (2019). Multimodality in Multi-Objective Optimization — More Boon than Bane?. In Deb, K., Goodman, E., Coello, C. C. A., Klamroth, K., Miettinen, K., Mostaghim, S., & Reed, P. (Eds.), Evolutionary Multi-Criterion Optimization (pp. 126–138). Lecture Notes in Computer Science: Vol. 11411. Springer.
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  • 2018

    Aufsatz (Zeitschrift)

    Carnein, M., & Trautmann, H. (2018). evoStream — Evolutionary Stream Clustering Utilizing Idle Times. Big Data Research, 14, 101–111.
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    Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation (ECJ), 26(4), 597–620.
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    Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2018). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ), 0, 1–30. (In press)
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    Li, L., Wang, Y., Trautmann, H., Jing, N., & Emmerich, M. (2018). Multiobjective evolutionary algorithms based on target region preferences. Swarm and Evolutionary Computation, 40, 196–215.
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    Aufsatz (Konferenz)

    Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18), Kyoto, Japan, 585–592.
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    Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18) Companion, Kyoto, Japan, 1737–1744.
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    van Engelen, J., van Lier, J., Takes, F., & Trautmann, H. (2018). Accurate WiFi based indoor positioning with continuous location sampling. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), Dublin, Ireland. (In press)
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  • 2017

    Aufsatz (Zeitschrift)

    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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    Aufsatz (Konferenz)

    Carnein, M., Assenmacher, D., & Trautmann, H. (2017). An Empirical Comparison of Stream Clustering Algorithms. In Proceedings of the ACM International Conference on Computing Frontiers (CF '17), Siena, Italy, 361–365.
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    Carnein, M., Assenmacher, D., & Trautmann, H. (2017). Stream Clustering of Chat Messages with Applications to Twitch Streams. In de Cesare, S., & Ulrich, F. (Eds.), Proceedings of the 36th International Conference on Conceptual Modeling (ER'17) (pp. 79–88). Springer International Publishing.
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    Carnein, M., Heuchert, M., Homann, L., Trautmann, H., Vossen, G., Becker, J., & Kraume, K. (2017). Towards Efficient and Informative Omni-Channel Customer Relationship Management. In de Cesare, S., & Ulrich, F. (Eds.), Proceedings of the 36th International Conference on Conceptual Modeling (ER'17) (pp. 69–78). Lecture Notes in Computer Science: Vol. 10651. Springer International Publishing.
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    Carnein, M., Homann, L., Trautmann, H., Vossen, G., & Kraume, K. (2017). Customer Service in Social Media — An Empirical Study of the Airline Industry. In Bernhard, M. a. N. R. a. H. S. a. M. K. a. A. T. a. O. K. a. M. W. (Ed.), Proceedings of the 17th Conference on Database Systems for Business, Technology, and Web (BTW '17) (pp. 33–40). Lecture Notes in Informatics (LNI): Vol. P-266. Gesellschaft für Informatik.
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    Buchbeitrag (Sammel-, Herausgeberband)

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

    Trautmann, H., Vossen, G., Homann, L., Carnein, M., & Kraume, K. (2017). Challenges of Data Management and Analytics in Omni-Channel CRM. In Becker, J., Backhaus, K., Dugas, M., Hellingrath, B., Hoeren, T., Klein, S., Kuchen, H., Trautmann, H., & Vossen, G. (Eds.), ERCIS Working Papers: Vol. 28. Münster: European Research Center for Information Systems.
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  • 2016

    Aufsatz (Zeitschrift)

    Neumann, F., & Trautmann, H. (2016). Working Group Report: Bridging the Gap Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of Evolutionary Algorithms (Dagstuhl Seminar 15211). Dagstuhl Reports, 5(5), 78–79.
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    Rudolph, G., Schütze, O., Grimme, C., Domínguez-Medina, C., & Trautmann, H. (2016). Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results. Computational Optimization and Applications, 64(2), 589–618.
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    Schütze, O., Sosa Hernandez VA, Trautmann, H., & Rudolph, G. (2016). The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems. Journal of Heuristics, 22(3), 273–300.
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    Aufsatz (Konferenz)

    Blot, A., Hoos, H., Jourdan, L., Marmion, M., & Trautmann, H. (2016). MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In Proceedings of the Learning and Intelligent Optimization, 10th International Conference, Ischia. (Accepted)
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    Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers. In Festa, P., Sellmann, M., & Vanschoren, J. (Eds.), Learning and Intelligent Optimization (pp. 48–59). Lecture Notes in Computer Science: Vol. 10079. Springer International Publishing.
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    Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. In Adorni, G., Cagnoni, S., Gori, M., & Maratea, M. (Eds.), AI*IA 2016 Advances in Artificial Intelligence (pp. 3–12). Lecture Notes in Computer Science: Vol. 10037. Cham: Springer.
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    Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '16), Denver, CO, USA, 229–236.
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    Kerschke, P., & Trautmann, H. (2016). The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Kanada.
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    Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes. In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), Edinburgh, Scotland, 962–972.
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    Buchbeitrag (Sammel-, Herausgeberband)

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

    Aufsatz (Zeitschrift)

    Brockhoff, D., Wagner, T., & Trautmann, H. (2015). R2 Indicator Based Multiobjective Search. Evolutionary Computation Journal, 23(3), 369–395.
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    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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    Aufsatz (Konferenz)

    Chinnov, A., Kerschke, P., Meske, C., Stieglitz, S., & Trautmann, H. (2015). An Overview of Topic Discovery in Twitter Communication through Social Media Analytics. In Proceedings of the 20th Americas Conference on Information Systems (AMCIS '15), Puerto Rico, 1–10.
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    Grimme, C., Meisel, S., Trautmann, H., Rudolph, G., & Wölck, M. (2015). Multi-Objective Analysis of Approaches to Dynamic Routing Of a Vehicle. In Proceedings of the European Conference On Information Systems, Münster, Germany.
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    Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2015). Detecting Funnel Structures by Means of Exploratory Landscape Analysis. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain, 265–272.
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    Kotthoff, L., Kerschke, P., Hoos, H. H., & Trautmann, H. (2015). Improving the State of the Art in Inexact TSP Solving using Per-Instance Algorithm Selection. In Dhaenens, C., Jourdan, L., & Marmion, M.-E. (Eds.), Learning and Intelligent Optimization, 9th International Conference (pp. 202–217). Cham: Springer International Publishing.
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    Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. In Proceedings of the Genetic and Evolutionary Computation Conference, Madrid, Spain, 425–432.
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    Sosa, H. V., Schütze, O., Trautmann, H., & Rudolph, G. (2015). On the Behavior of Stochastic Local Search within Parameter Dependent MOPs. In Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, Guimaraes, Portugal, 126–140.
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    Kurzbeitrag (Poster)

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

    Martí, L., Grimme, C., Kerschke, P., Trautmann, H., & Rudolph, G. (2015). Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms.
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  • 2014

    Aufsatz (Konferenz)

    Rudolph, G., Grimme, C., Schütze, O., & Trautmann, H. (2014). An Aspiration Set EMOA Based on Averaged Hausdorff Distances. In Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8), Gainesville, Florida, USA, 153–156.
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    Wessing, S., Preuss, M., & Trautmann, H. (2014). Stopping Criteria for Multimodal Optimization. In Proceedings of the Parallel Problem Solving from Nature — PPSN XIII, Ljubljana, Slovenia, 141–150.
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    Buchbeitrag (Sammel-, Herausgeberband)

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

    Aufsatz (Zeitschrift)

    Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. Annals of Mathematics and Artificial Intelligence, 69, 151–182.
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    Trautmann, H., Wagner, T., Biermann, D., & Weihs, C. (2013). Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index. Journal of Multi-Criteria Decision Analysis, 20(5-6), 319–337.
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    Aufsatz (Konferenz)

    Dominguez-Medina, C., Rudolph, G., Schütze, O., & Trautmann, H. (2013). Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, 3190–3197.
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    Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., & Trautmann, H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. In Proceedings of the FOGA, Adelaide, Australia, 147–160.
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    Preuss, M., Kozakowski, D., Hagelbäck, J., & Trautmann, H. (2013). Reactive strategy choice in StarCraft by means of Fuzzy Control. In Proceedings of the 2013 IEEE Conference on Computational Inteligence in Games (CIG), Niagara Falls, ON, Canada, 1–8.
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    Rudolph, G., Trautmann, H., Sengupta, S., & Schütze, O. (2013). Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation. In Purshouse, R., Fleming, P., Fonseca, C., Greco, S., & Shaw, J. (Eds.), Evolutionary Multi-Criterion Optimization — 7th International Conference, EMO 2013, Sheffield, UK, Proceedings (pp. 443–458). Lecture Notes in Computer Science: Vol. 7811. Springer.
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    Sosa-Hernandez VA, Schütze, O., Rudoph, G., & Trautmann, H. (2013). Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms. In Proceedings of the GECCO 2013, Amsterdam (Netherlands), 1699–1702.
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    Trautmann, H., Wagne, T., & Brockhoff, D. (2013). R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In Proceedings of the Learning and Intelligent Optimization Conference 7, Catania, Italy, 70–74.
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    Wagner, T., Trautmann, H., & Brockhoff, D. (2013). Preference Articulation by Means of the R2 Indicator. In Purshouse, R. C., Fleming, P. J., Fonseca, C. M., Greco, S., & Shaw, J. (Eds.), Evolutionary Multi-Criterion Optimization — 7th International Conference, EMO 2013, Sheffield, UK, Proceedings (pp. 81–95). Lecture Notes in Computer Science: Vol. 7811. Springer.
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    Buchbeitrag (Sammel-, Herausgeberband)

    Sosa, H. V., Schütze, O., Rudolph, G., & Trautmann, H. (2013). The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume. In Emmerich, M., Deutz, A., Schuetze, O., Bäck, T., Tantar, A., Moral, P., Legrand, P., Bouvry, P., & Coello, C. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV (pp. 189–205). Advances in Intelligent Systems and Computing: Vol. 227. Springer International Publishing.
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    Trautmann, H., Rudolph, G., Dominguez-Medina, C., & Schütze, O. (2013). Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems. In Schütze, O., Coello, C. C., Tantar, A., Tantar, E., Bouvry, P., Del, M. P., & Legrand, P. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II (pp. 89–105). Advances in Intelligent Systems and Computing: Vol. 175. Springer Berlin Heidelberg.
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  • 2012

    Aufsatz (Zeitschrift)

    Bischl, B., Mersmann, O., Trautmann, H., & Weihs, C. (2012). Resampling Methods in Model Validation. Evolutionary Computation Journal, 20(2), 249–275.
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    Rudolph, G., Trautmann, H., & Schütze, O. (2012). Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. at-Automatisierungstechnik, 60, 610–621.
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    Aufsatz (Konferenz)

    Bischl, B., Mersmann, O., Trautmann, H., & Preuss, M. (2012). Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In Soule, T., & Moore, J. (Eds.), Genetic and Evolutionary Computation Conference, GECCO '12, Philadelphia, PA, USA (pp. 313–320). ACM.
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    Brockhoff, D., Wagner, T., & Trautmann, H. (2012). On the Properties of the R2 Indicator. In Soule, T., & others, (Eds.), Proc. 14th Int'l. Genetic and Evolutionary Computation Conference (GECCO '12) (pp. 465–472). ACM.
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    Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Hamadi, Y., & Schoenauer, M. (Eds.), Learning and Intelligent Optimization — 6th International Conference, LION 6, Paris (pp. 115–129). Lecture Notes in Computer Science: Vol. 7219. Springer.
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  • 2011

    Aufsatz (Zeitschrift)

    Naujoks, B., Trautmann, H., Wessing, S., & Weihs, C. (2011). Advanced concepts for multi-objective evolutionary optimization in aircraft industry. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225, 1081–1096.
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    Aufsatz (Konferenz)

    Gerstl, K., Rudolph, G., Schütze, O., & Trautmann, H. (2011). Finding evenly spaced fronts for multiobjective control via averaging Hausdorff-measure. In Proceedings of 8th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 1–6.
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    Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., & Rudolph, G. (2011). Exploratory landscape analysis. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, 829–836.
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    Wagner, T., Trautmann, H., & Marti, L. (2011). A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms. In Takahashi, R., Deb, K., Wanner, E., & Greco, S. (Eds.), Evolutionary Multi-Criterion Optimization (pp. 16–30). Lecture Notes in Computer Science: Vol. 6576. Springer Berlin — Heidelberg.
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  • 2010

    Aufsatz (Zeitschrift)

    Azene, Y., Roy, R., Farrugia, D., Onisa, C., Mehnen, J., & Trautmann, H. (2010). Work roll cooling system design optimisation in presence of uncertainty and constrains. CIRP Journal of Manufacturing Science and Technology, 2, 290–298.
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    Wagner, T., & Trautmann, H. (2010). Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions. IEEE Transactions on Evolutionary Computation, 14(5), 688–701.
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    Aufsatz (Konferenz)

    Bischl, B., Mersmann, O., & Trautmann, H. (2010). Resampling Methods in Model Validation. In Bartz-Beielstein, T., Chiarandini, M., Paquete, L., & Preuss, M. (Eds.), Proceedings of the Workshop on Experimental Methods for the Assessment of Computational Systems (WEMACS 2010), Algorithm Engineering Report TR10-2-007. Department of Computer Science, TU Dortmund University.
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    Ding, J., Wessing, S., Trautmann, H., Mehnen, J., & Naujoks, B. (2010). Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing. In Teti, R. (Ed.), Proceedings of the 7th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '10). Capri, Italy: Copyright C.O.C. Com. org. Conv.
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    Mersmann, O., Preuss, M., & Trautmann, H. (2010). Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In Schaefer, R., Cotta, C., Kolodziej, J., & Rudolph, G. (Eds.), 11th International Conference on Parallel Problem Solving from Nature — PPSN XI, Proceedings, Part I (pp. 73–82). Lecture Notes in Computer Science: Vol. 6238. Springer.
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    Mersmann, O., Trautmann, H., Naujoks, B., & Weihs, C. (2010). On the Distribution of EMOA Hypervolumes. In Blum, C., & Battiti, R. (Eds.), Learning and Intelligent Optimization, 4th International Conference, LION 4, Venice, Italy (pp. 333–337). Lecture Notes in Computer Science: Vol. 6073. Springer.
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    Mersmann, O., Trautmann, H., Naujoks, B., & Weihs, C. (2010). Benchmarking evolutionary multiobjective optimization algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 1–8.
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    Mostaghim, S., Trautmann, H., & Mersmann, O. (2010). Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities. In Schaefer, R., Cotta, C., Kolodziej, J., & Rudolph, G. (Eds.), 11th International Conference on Parallel Problem Solving from Nature — PPSN XI, Proceedings, Part II (pp. 101–110). Lecture Notes in Computer Science: Vol. 6239. Springer.
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    Voss, T., Trautmann, H., & Igel, C. (2010). New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization. In Schaefer, R., Cotta, C., Kolodziej, J., & Rudolph, G. (Eds.), 11th International Conference on Parallel Problem Solving from Nature — PPSN XI, Proceedings, Part II (pp. 260–269). Lecture Notes in Computer Science: Vol. 6239. Springer.
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    Wagner, T., & Trautmann, H. (2010). Online convergence detection for evolutionary multi-objective algorithms revisited. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 1–8.
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  • 2009

    Aufsatz (Zeitschrift)

    Trautmann, H., & Mehnen, J. (2009). Statistical Methods for Improving Multi-objective Evolutionary Optimisation. International Journal of Computational Intelligence Research, 5(2), 72–78.
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    Trautmann, H., & Mehnen, J. (2009). Preference-Based Pareto-Optimization in Certain and Noisy Environments. Engineering Optimization, 41, 23–38.
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    Trautmann, H., Wagner, T., Naujoks, B., Preuss, M., & Mehnen, J. (2009). Statistical Methods for Convergence Detection of Multi-Objective Evolutionary Algorithms. Evolutionary Computation, Special Issue: Twelve Years of EC Research in Dortmund, 17(4), 493–509.
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    Aufsatz (Konferenz)

    Naujoks, B., & Trautmann, H. (2009). Online Convergence Detection for Multiobjective Aerodynamic Applications. In Tyrrell, A. (Ed.), Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway (pp. 332–339). IEEE Press.
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    T., W. H. T., & Naujoks, B. (2009). OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing. In Fonseca, C., & Gandibleux, X. (Eds.), Evolutionary Multi-Criterion Optimization (EMO 2009), Lecture Notes in Computer Science (LNCS) 5467 (pp. 198–215). Springer, Berlin.
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    Trautmann, H., Mehnen, J., & Naujoks, B. (2009). Pareto-Dominance in Noisy Environments. In Tyrrell, A. (Ed.), Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway (pp. 3119–3126). IEEE Press.
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  • 2008

    Aufsatz (Konferenz)

    Mehnen, J., & Trautmann, H. (2008). Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing. In Teti, R. (Ed.), Proceedings of the 6th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '08). Naples, Italy: Copyright C.O.C. Com. org. Conv.
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    Trautmann, H., Ligges, U., Mehnen, J., & Preuss, M. (2008). A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing. In Rudolph, G., & others, (Eds.), Parallel Problem Solving from Nature (PPSN) (pp. 825–836). Springer, Berlin.
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  • 2007

    Aufsatz (Konferenz)

    Mehnen, J., Trautmann, H., & Tiwari, A. (2007). Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes. In CEC 2007, IEEE Congress on Evolutionary Computation, 2687–2694.
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    Weihs, C., & Trautmann, H. (2007). Parallel Universes: Multi-Criteria Optimization. In Berthold, M., Morik, K., & Siebes, A. (Eds.), Dagstuhl Seminar Proceedings 07181, Parallel Universes and Local Patterns. Schloss Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum f�r Informatik (IBFI).
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  • 2006

    Aufsatz (Zeitschrift)

    Trautmann, H., & Weihs, C. (2006). On the Distribution of the Desirability Index using Harrington's Desirability Function. Metrika, 63(2), 207–213.
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    Aufsatz (Konferenz)

    Mehnen, J., & Trautmann, H. (2006). Integration of Expert's Preferences in Pareto Optimization by Desirability Function Techniques. In Teti, R. (Ed.), CIRP ICME '06) — Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (pp. 293–298). Ischia, Italy: C.O.C. Com. org. Conv. CIRP ICME '06.
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  • 2004

    Abschlussarbeit (Dissertation, Habilitation)

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