• 2023

    Forschungsartikel in Sammelband (Konferenz)

    Prager, R., & Trautmann, H. (2023). Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In Correia, J., Smith, S., & Qaddoura, R. (Eds.), Applications of Evolutionary Computation (pp. 411–425). Cham: Springer Nature Switzerland.
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    Schäpermeier, L., Kerschke, P., Grimme, C., & Trautmann, H. (2023). Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In Li, K., & Wang, H. (Eds.), Proceedings of the International Conference Series on Evolutionary Multi-Criterion Optimization (pp. 291–304). Lecture Notes in Computer Science: Vol. 13970. Cham: Springer.
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    Forschungsartikel (Zeitschrift)

    Prager, R. P., & Trautmann, H. (2023). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary Computation. (accepted / in press (not yet published))
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  • 2022

    Forschungsartikel (Buchbeitrag)

    Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Becker, J., & Trautmann, H. (2022). (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In Weitzel, G., & Mündges, S. (Eds.), Hate Speech — Definitionen, Ausprägungen, Lösungen (pp. 249–274). Wiesbaden: VS Verlag für Sozialwissenschaften.
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    Forschungsartikel in Sammelband (Konferenz)

    Assenmacher, D., & Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In Tran, T. e. a. (Ed.), Intelligent Information and Database Systems (pp. 3–16). Cham: Springer International Publishing.
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    Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In Rudolph, G., Kononova, A., Aguirre, H., Kerschke, P., Ochoa, G., & Tu{š}ar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Cham: Springer International Publishing.
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    Pohl, J. S., Assenmacher, D., Seiler, M. V., Trautmann, H., & Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In Association, f. t. A. o. A. I. (. (Ed.), Workshop Proceedings of the 16th International Conference on Web and Social Media (ICWSM) (pp. 1–10). Palo Alto, CA, USA: AAAI Press.
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    Prager, R. P., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Cham: Springer International Publishing.
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    Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In Fieldsend, J., & Wagner, M. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 356–359-356–359). GECCO '22. New York, NY, USA: Association for Computing Machinery.
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    Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., & Kerschke, P. (2022). HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In Rudolph, G., Kononova, A. V., Aguirre, H., Kerschke, P., Ochoa, G., & Tušar, T. (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 575–589). Cham: Springer International Publishing.
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    Seiler, M. V., Prager, R. P., Kerschke, P., & Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In -, (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (pp. 657–665). New York, NY, USA: Association for Computing Machinery.
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    Forschungsartikel (Zeitschrift)

    Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H., & Grimme, C. (2022). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing, 22(2), 271–285.
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    Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(8), 1–44.
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    Heins, J., Bossek, J., Pohl, J. S., Seiler, M. V., Trautmann, H., & Kerschke, P. (2022). A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection. Theoretical Computer Science (Theoret. Comput. Sci.), 940.
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    Abstract in Sammelband (Konferenz)

    Leszkiewicz, A., Bucur, D., Grimme, C., Michalski, R., Clever, L., Pohl, J. S., Rook, J., Bossek, J., Preuss, M., Squillero, G., Quer, S., Calabrese, A., Iacca, G., Kizgin, H., & Trautmann, H. (2022). Social Influence Analysis (SIA) in Online Social Networks.
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  • 2021

    Forschungsartikel in Sammelband (Konferenz)

    Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., & Grimme, C. (2021). Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In Ishibuchi, H. e. a. (Ed.), Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 311–322). Heidelberg, Berlin: Springer.
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    Assenmacher, D., Niemann, M., Müller, K., Seiler, M. V., Riehle, D. M., & Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), Virtual Event, 1–14.
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    Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In Association, f. C. M. (Ed.), Proceedings of the 16th ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Dornbirn, Austria: Association for Computing Machinery.
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    Prager, R. P., Moritz, V. H., & Pascal, (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In Proceedings of the IEEE Symposium Series on Computational Intelligence, Orlando, Florida, USA.
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    Forschungsartikel (Zeitschrift)

    Assenmacher, D., Weber, D., Preuss, M., Calero, V. A., Bradshaw, A., Ross, B., Cresci, S., Trautmann, H., Neumann, F., & Grimme, C. (2021). Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social Science Computer Review, online first.
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    Coombs, C., Stacey, P., Kawalek, P., Simeonova, B., Becker, J., Bergener, K., Carvalho, J. Á., Fantinato, M., Garmann-Johnsen, N. F., Grimme, C., Stein, A., & Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. International Journal of Information Management, 58.
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    Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A., Wang, H., & Emmerich, M. (2021). Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Computers & Operations Research, 136, 105489.
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  • 2020

    Forschungsartikel in Sammelband (Konferenz)

    Assenmacher, D., Adam, L., Trautmann, H., & Grimme, C. (2020). Towards Real-Time and Unsupervised Campaign Detection in Social Media. In Proceedings of the Florida Artificial Intelligence Research Society Conference, Florida, USA.
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    Assenmacher, D., Clever, L., Pohl, J., Trautmann, H., & Grimme, C. (2020). A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In Meiselwitz, G. (Ed.), Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis (pp. 201–214). Cham: Springer International Publishing.
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    Assenmacher, D., & Adam, L. &. F. L. &. T. H. &. G. C. (2020). Inside the tool set of automation: Free social bot code revisited. In Grimme, C., Preuß, M., Takes, F., & Waldherr, A. (Eds.), Disinformation in open online media (pp. 101–114). Lecture Notes in Computer Science. Wiesbaden: Springer.
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    Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 1–8.
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    Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20), Cancun, Mexico, 166–174.
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    Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 1–8.
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    Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). Towards Automated Configuration of Stream Clustering Algorithms. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD '19), Würzburg, Germany, 137–143.
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    Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2020). confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. In Proceedings of the 14th Learning and Intelligent Optimization Conference (LION 2020), Athens, Greece, 80–95.
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    Lena, C., Frischlich, L., Trautmann, H., & Grimme, C. (2020). Automated detection of nostalgic text in the context of societal pessimism. In Proceedings of the MISDOOM 2019, Hamburg, Deutschland, 48–58.
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    Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., & Kerschke, P. (2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 996–1003.
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    Riehle, D. M., Niemann, M., Brunk, J., Assenmacher, D., Trautmann, H., & Becker, J. (2020). Building an Integrated Comment Moderation System — Towards a Semi-Automatic Moderation tool. In Proceedings of the HCI International 2020, Copenhagen, Denmark.
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    Seiler, M. V., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 48–64.
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    Seiler, M. V., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 1–8.
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    Steinhoff, V., Kerschke, P., Aspar, P., Trautmann, H., & Grimme, C. (2020). Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2445–2452.
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    Forschungsartikel (Zeitschrift)

    Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, , Heike, , & Grimme, C. (2020). Demystifying social bots: On the intelligence of automated social media actors. Social media — society, 00.
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    Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 2020(88), 105901.
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  • 2019

    Fachbuch (Herausgegebenes Buch)

    Bauer, N., Ickstadt, K., Lübke, K., Szepannek, G., Trautmann, H., & Vichi, M. (Eds.) (2019). Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing.
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    Forschungsartikel (Buchbeitrag)

    Kerschke, P., & Trautmann, H. (2019). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. In Bauer, N., Ickstadt, K., Lübke, K., Szepannek, G., Trautmann, H., & Vichi, M. (Eds.), Applications in Statistical Computing (pp. 93–123). Springer.
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    Forschungsartikel in Sammelband (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. Springer International Publishing.
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    Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In Proceedings of the 15th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 58–71.
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    Carnein, M., Homann, L., Trautmann, H., & Vossen, G. (2019). A Recommender System Based on Omni-Channel Customer Data. In Proceedings of the 21st IEEE Conference on Business Informatics (CBI' 19), Moscow, Russia, 65–74.
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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, 280–292.
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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 Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO), East Lansing, MI, USA, 126–138.
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    Forschungsartikel (Zeitschrift)

    Carnein, M., & Trautmann, H. (2019). Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. Business and Information Systems Engineering (BISE), 61(3), 277–297.
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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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    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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  • 2018

    Forschungsartikel in Sammelband (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, 524–540.
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    Forschungsartikel (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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    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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  • 2017

    Forschungsartikel (Buchbeitrag)

    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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    Forschungsartikel in Sammelband (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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    Forschungsartikel (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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    Arbeitspapier / Working Paper

    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

    Forschungsartikel (Buchbeitrag)

    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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    Forschungsartikel in Sammelband (Konferenz)

    Blot, A., Hoos, H., Jourdan, L., Marmion, M., & Trautmann, H. (2016). MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In Joaquin, V. e. a. (Ed.), LION 2016: Learning and Intelligent Optimization (pp. 32–47). LNTCS: Vol. 10079. Cham: Springer International Publishing.
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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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    Forschungsartikel (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 (Comput. Optim. Appl.), 64(2), 589–618.
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    Schütze, O., Sosa, H. V., Trautmann, H., & Rudolph, G. (2016). The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems. Journal of Heuristics, 22(3), 273–300.
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  • 2015

    Forschungsartikel in Sammelband (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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    Abstract in Online-Sammlung (Konferenz)

    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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    Forschungsartikel (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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    Forschungsartikel in Online-Sammlung

    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

    Forschungsartikel (Buchbeitrag)

    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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    Forschungsartikel in Sammelband (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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  • 2013

    Forschungsartikel (Buchbeitrag)

    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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    Forschungsartikel in Sammelband (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, V., Schütze, O., Rudoph, G., & Trautmann, H. (2013). Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms. In Proceedings of the GECCO 2013, Amsterdam (Netherlands), 1699–1702.
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    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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    Forschungsartikel (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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  • 2012

    Forschungsartikel in Sammelband (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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    Forschungsartikel (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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  • 2011

    Forschungsartikel in Sammelband (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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    Forschungsartikel (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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  • 2010

    Forschungsartikel in Sammelband (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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    Forschungsartikel (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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  • 2009

    Forschungsartikel in Sammelband (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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    Forschungsartikel (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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  • 2008

    Forschungsartikel in Sammelband (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

    Forschungsartikel in Sammelband (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 (pp. ). Schloss Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum f�r Informatik (IBFI).
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  • 2006

    Forschungsartikel in Sammelband (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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    Forschungsartikel (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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  • 2004

    Qualifikationsschrift (Dissertation, Habilitationsschrift)

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