Prof. Dr. Heike Trautmann

Kontakt

Portrait
Wirtschaftsinformatik und StatistikLeonardo-Campus 3
Raum 309
48149 Münster
Sprechzeiten: nach Vereinbarung

Forschungsschwerpunkte

  • Mehrkriterielle Optimierung
  • Evolutionäre Algorithmen / Künstliche Intelligenz
  • Algorithmenselektion
  • Statistische Qualitätssicherung
  • Diskrete Optimierung

Vita

Akademische Ausbildung

07/2004 - 01/2013Habilitation im Fach Statistik: ”Statistical and Experimental Methods in Single- and Multi- Objective (Evolutionary) Optimization”, TU Dortmund, Deutschland
10/2002 - 06/2004Promotion im Fach Statistik: „Qualitätskontrolle in der Industrie anhand von Kontrollkarten für Wünschbarkeitsindizes - Anwendungsfeld Lagerverwaltung“, Graduate School of Production Engineering and Logistics, Universität Dortmund, Deutschland
10/1997 - 02/2000Studium der Statistik, Universität Dortmund, Deutschland
10/1996 - 07/1998Studium der Wirtschaftsmathematik, Universität Dortmund, Deutschland

Beruflicher Werdegang

seit 10/2016Prodekanin für Internationales, Wirtschaftswissenschaftliche Fakultät
seit 04/2013Professorin für Wirtschaftsinformatik und Statistik, WWU Münster, Deutschland
02/2017 - 01/2018Pascal Professorin, LIACS, Universität Leiden, Niederlande
10/2007 - 03/2013Postdoktorandin, Lehrstuhl für Computergestützte Statistik, TU Dortmund, Deutschland
10/2006 - 09/2007DFG Forschungsstipendium: „Der Einfluss von Modellunsicherheiten bei Wünschbarkeitsindizes und Pareto-Optimierung“, TU Dortmund, Deutschland
07/2004 - 09/2006Graduiertenkolleg Statistische Modellbildung, Universität Dortmund. Deutschland, Postdoktorandenstipendium
10/2002 - 06/2004Graduate School of Production Engineering and Logistics, Universität Dortmund, Deutschland, Doktorandenstipendium
03/2001 - 09/2002Roland Berger Strategy Consultants, München, Deutschland, Business Analytics Consultant
04/2000 - 02/2001Marketing Systems GmbH, Essen, Germany, Consultant

Mitgliedschaften und Aktivitäten in Gremien

seit 2013Foundations of Genetic Algorithms (FOGA), Mitglied im Programmkomittee
seit 2012EVOLVE - A bridge between Probability, Set Oriented Numerics and Evolutionary Computation, Mitglied im Programmkomitee
seit 2012ACM-SIGEVO, Special Interest Group for Genetic and Evolutionary Computation, Association for Computing Machinery
seit 2011Genetic and Evolutionary Computation Conference (GECCO) , Mitglied im Programmkomitee
seit 2008International Conference on Parallel Problem Solving From Nature (PPSN), Mitglied im Programmkomitee
seit 2007IEEE Congress on Evolutionary Computation (CEC), Mitglied im Programmkomitee
10/2011 - 03/2013Global Young Faculty, Stiftung Mercator

Publikationen

  • Bossek Jakob, Grimme Christian, Meisel Stephan, Rudolph Günter, Trautmann Heike. 2019. ‘Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.’Contributed to the Proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO), East Lansing, Michigan, USA. [Akzeptiert]
  • Carnein Matthias, Trautmann Heike. 2019. ‘Customer Segmentation Based on Transactional Data Using Stream Clustering.’Contributed to the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '19), Macau, China. [Akzeptiert]
  • Grimme Christian, Kerschke Pascal, Trautmann Heike. 2019. ‘Multimodality in Multi-Objective Optimization -- More Boon than Bane?.’Contributed to the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO), East Lansing, MI, USA. [Akzeptiert]
  • Kerschke Pascal, Kotthoff Lars, Bossek Jakob, Hoos Holger H., Trautmann Heike. 2018. ‘Leveraging TSP Solver Complementarity through Machine Learning.’ Evolutionary Computation (ECJ) 26, Nr. 4: 597-620. doi: 10.1162/evco_a_00215.
  • Carnein Matthias, Trautmann Heike. 2018. ‘evoStream - Evolutionary Stream Clustering Utilizing Idle Times.’ Big Data Research 14: 101-111. doi: 10.1016/j.bdr.2018.05.005.
  • Kerschke Pascal, Wang Hao, Preuss Mike, Grimme Christian, Deutz André, Trautmann Heike, Emmerich Michael. 2018. ‘Search Dynamics on Multimodal Multi-Objective Problems.’ Evolutionary Computation (ECJ) 0: 1-30. doi: 10.1162/evco_a_00234. [Im Druck]
  • Kerschke Pascal, Trautmann Heike. 2018. ‘Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.’ Evolutionary Computation (ECJ) 2018. doi: 10.1162/evco_a_00236. [Im Druck]
  • Grimme Christian, Kerschke Pascal, Emmerich Michael T M, Preuss Mike, Deutz André H, Trautmann Heike. 2018. ‘Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.’Contributed to the International Global Optimization Workshop (LeGO 2018), Leiden, The Netherlands. [Akzeptiert]
  • 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. doi: https://doi.org/10.1016/j.swevo.2018.02.006.
  • Kerschke Pascal, Hoos Holger H, Neumann Frank, Trautmann H. 2018. ‘Automated Algorithm Selection: Survey and Perspectives.’ Evolutionary Computation (ECJ) 2018: 1-47. doi: 10.1162/evco_a_00242. [Im Druck]
  • van Engelen J.E., van Lier J.J., Takes F.W., Trautmann H. 2018. ‘Accurate WiFi based indoor positioning with continuous location sampling.’Contributed to the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), Dublin, Ireland. [Im Druck]
  • Bossek Jakob, Grimme Christian, Meisel Stephan, Rudolph Guenter, Trautmann Heike. 2018. ‘Local Search Effects in Bi-Objective Orienteering.’Contributed to the Genetic and Evolutionary Computation Conference (GECCO '18), Kyoto, Japan. [Akzeptiert]
  • Bossek J, Trautmann H. 2018. ‘Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.’Contributed to the 12th International Conference on Learning and Intelligent Optimization, Kalamata, Greece. [Akzeptiert]
  • Kerschke Pascal, Bossek Jakob, Trautmann Heike. 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, 1737-1744. doi: 10.1145/3205651.3208233.
  • Carnein Matthias, Trautmann Heike. 2018. ‘Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.’ Business and Information Systems Engineering (BISE) 2018. [Akzeptiert]
  • Trautmann Heike, Vossen Gottfried, Homann Leschek, Carnein Matthias, Kraume Karsten. 2017. Challenges of Data Management and Analytics in Omni-Channel CRM , Nr. 28. Münster: European Research Center for Information Systems, 2017.
  • Grimme, C., Frischlich, L., Preuß, M., Assenmacher, D., Adam, L., Trautmann, H., & Quandt, T. 2017. Projekt 345: Validierung der automatisierten Erkennung von Social Bots. , 2017. [Akzeptiert]
  • Tierney K, Handali J, Grimme C, Trautmann H. 2017. ‘Multi-objective Optimization for Liner Shipping Fleet Repositioning.’ In Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited by Trautmann H, Rudolph G, Klamroth K, Schütze O, Wiecek M, Jin Y, Grimme C, 622-638. Cham: Springer International Publishing. doi: 10.1007/978-3-319-54157-0_42.
  • Grimme C, Preuss M, Adam L, Trautmann H. 2017. ‘Social Bots: Human-Like by Means of Human Control?.’ Big Data 5, Nr. 4: 279-293. doi: 10.1089/big.2017.0044.
  • Adrián Sosa Hernández V, Lara A, Trautmann H, Rudolph G, Schütze O. 2017. ‘The Directed Search Method for Unconstrained Parameter Dependent Multi-objective Optimization Problems.’ In NEO 15, edited by Schütze O, Trujillo L, Legrand P, Maldonado Y, 281-330. Cham: Springer International Publishing. doi: 10.1007/978-3-319-44003-3_12.
  • 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 Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings, edited by Trautmann H, Rudolph G, Klamroth K, Schütze O, Wiecek M, Jin Y, Grimme C, 406-421. Cham: Springer International Publishing. doi: 10.1007/978-3-319-54157-0_28.
  • Carnein Matthias, Heuchert Markus, Homann Leschek, Trautmann Heike, Vossen Gottfried, Becker Jörg, Kraume Karsten. 2017. ‘Towards Efficient and Informative Omni-Channel Customer Relationship Management.’ In Proceedings of the 36th International Conference on Conceptual Modeling (ER'17), edited by de Cesare Sergio, Ulrich Frank, 69-78.: Springer International Publishing. doi: 10.1007/978-3-319-70625-2_7.
  • Carnein Matthias, Assenmacher Dennis, Trautmann Heike. 2017. ‘An Empirical Comparison of Stream Clustering Algorithms.’ In Proceedings of the ACM International Conference on Computing Frontiers (CF '17), 361-365. doi: 10.1145/3075564.3078887.
  • Carnein Matthias, Homann Leschek, Trautmann Heike, Vossen Gottfried, Kraume Karsten. 2017. ‘Customer Service in Social Media - An Empirical Study of the Airline Industry.’ In Proceedings of the 17th Conference on Database Systems for Business, Technology, and Web (BTW '17), edited by Bernhard Mitschang and Norbert Ritter and Holger Schwarz and Meike Klettke and Andreas Thor and Oliver Kopp and Matthias Wieland, 33-40.: Gesellschaft für Informatik.
  • Carnein Matthias, Assenmacher Dennis, Trautmann Heike. 2017. ‘Stream Clustering of Chat Messages with Applications to Twitch Streams.’ In Proceedings of the 36th International Conference on Conceptual Modeling (ER'17), edited by de Cesare Sergio, Ulrich Frank, 79-88.: Springer International Publishing. doi: 10.1007/978-3-319-70625-2_8.
  • Blot A, Hoos H, Jourdan L, Marmion M, Trautmann H. 2016. ‘MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework.’Contributed to the Learning and Intelligent Optimization, 10th International Conference, Ischia. [Akzeptiert]
  • 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, Nr. 3: 273-300. doi: 10.1007/s10732-016-9310-0.
  • Rudolph G, Schütze O, Trautmann H. 2016. ‘On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front.’ In Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part II, edited by Squillero G, Burelli P, 42-55. Cham: Springer International Publishing. doi: 10.1007/978-3-319-31153-1_4.
  • Bossek J, Trautmann H. 2016. ‘Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.’ In Proceedings of AI*IA 2016, edited by Adorni G et al., 3-12. doi: 10.1007/978-3-319-49130-1 1.
  • Kerschke Pascal, Wang Hao, Preuss Mike, Grimme Christian, Deutz André, Trautmann Heike, Emmerich Michael. 2016. ‘Towards Analyzing Multimodality of Multiobjective Landscapes.’ In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN XIV), 962-972.: Springer. doi: 10.1007/978-3-319-45823-6_90.
  • 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, Nr. 2: 589-618. doi: 10.1007/s10589-015-9815-8.
  • Kerschke Pascal, Trautmann Heike. 2016. ‘The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems.’Contributed to the IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Kanada. doi: 10.1109/CEC.2016.7748359.
  • Bossek J, Trautmann H. 2016. ‘Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers.’Contributed to the 10th International Conference on Learning and Intelligent Optimization, Ischia, Italy. [Akzeptiert]
  • 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, Nr. 5: 78-79. doi: http://dx.doi.org/10.4230/DagRep.5.5.57.
  • Kerschke Pascal, Preuss Mike, Wessing Simon, Trautmann Heike. 2016. ‘Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models.’ In Proceedings of the 18th Annual Conference on Genetic and Evolutionary Computation, 229-236. doi: doi.org/10.1145/2908812.2908845.
  • Kotthoff Lars, Kerschke Pascal, Hoos Holger H, Trautmann Heike. 2015. ‘Improving the State of the Art in Inexact TSP Solving using Per-Instance Algorithm Selection.’ In Learning and Intelligent Optimization, 9th International Conference, edited by Dhaenens Clarisse, Jourdan Laetitia, Marmion Marie-Eléonore, 202-217. Cham: Springer International Publishing. doi: 10.1007/978-3-319-19084-6_18.
  • Kerschke Pascal, Preuss Mike, Wessing Simon, Trautmann Heike. 2015. ‘Detecting Funnel Structures by Means of Exploratory Landscape Analysis.’Contributed to the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain. doi: 10.1145/2739480.2754642.
  • Chinnov Andrey, Kerschke Pascal, Meske Christian, Stieglitz Stefan, Trautmann Heike. 2015. ‘An Overview of Topic Discovery in Twitter Communication through Social Media Analytics.’Contributed to the 20th Americas Conference on Information Systems (AMCIS '15), Puerto Rico.
  • Martí Luis, Grimme Christian, Kerschke Pascal, Trautmann Heike, Rudolph Günter. 2015. Averaged Hausdorff Approximations of Pareto Fronts based on Multiobjective Estimation of Distribution Algorithms.
  • Martí Luis, Grimme Christian, Kerschke Pascal, Trautmann Heike, Rudolph Günter.Averaged Hausdorff Approximations of Pareto Fronts Based on Multiobjective Estimation of Distribution Algorithms.“Poster contributed to the Genetic and Evolutionary Computation Conference (GECCO '15), Madrid, Spain, 2015. doi: 10.1145/2739482.2764631.
  • Mersmann O, Preuss M, Trautmann H, Bischl B, Weihs C. 2015. ‘Analyzing the BBOB Results by Means of Benchmarking Concepts.’ Evolutionary Computation Journal 23, Nr. 1: 161-185.
  • Brockhoff D, Wagner T, Trautmann H. 2015. ‘R2 Indicator Based Multiobjective Search.’ Evolutionary Computation Journal 23, Nr. 3: 369-395. doi: 10.1162/EVCO_a_00135.
  • Meisel Stephan, Grimme Christian, Bossek Jakob, Wölck Martin, Rudolph Guenter, Trautmann Heike. 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 (GECCO '15), 425-432. doi: 10.1145/2739480.2754705.
  • Sosa Hernandez V, Schütze O, Trautmann H, Rudolph G. 2015. ‘On the Behavior of Stochastic Local Search within Parameter Dependent MOPs.’ In Evolutionary Multi-Criterion Optimization - 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part II, edited by Gaspar-Cunha, António; Henggeler Antunes, Carlos; Coello Coello, Carlos, 126-140. doi: 10.1007/978-3-319-15892-1_9.
  • Grimme C, Meisel S, Trautmann H, Rudolph G, Wölck M. 2015. ‘Multi-Objective Analysis of Approaches to Dynamic Routing Of a Vehicle.’Contributed to the European Conference On Information Systems, Münster, Germany.
  • Kerschke Pascal, Preuss Mike, Hernández Carlos, Schütze Oliver, Sun Jian-Qiao, Grimme Christian, Rudolph Günter, Bischl Bernd, Trautmann Heike. 2014. ‘Cell Mapping Techniques for Exploratory Landscape Analysis.’ In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, edited by Tantar Alexandru-Adrian, Tantar Emilia, Sun Jian-Qiao, Zhang Wei, Ding Qian, Schütze Oliver, Emmerich Michael T M, Legrand Pierrick, Del Moral Pierre, Coello Coello Carlos A, 115-131. Cham: Springer International Publishing. doi: 10.1007/978-3-319-07494-8_9.
  • Rudolph Günter, Grimme Christian, Schütze Oliver, Trautmann Heike. 2014. ‘An Aspiration Set EMOA Based on Averaged Hausdorff Distances.’Contributed to the Learning and Intelligent OptimizatioN Conference (LION 8), Gainesville, Florida, USA.
  • Rudolph G, Schütze O, Grimme C, Trautmann H. 2014. ‘A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets.’ In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, edited by Tantar A, Tantar E, Sun J, Zhang W, Ding Q, Schütze O, Emmerich M, Legrand P, Del Moral P, Coello Coello CA, 261-273.: Springer International Publishing. doi: 10.1007/978-3-319-07494-8_18.
  • Wessing S, Preuss M, Trautmann H. 2014. ‘Stopping Criteria for Multimodal Optimization.’Contributed to the Parallel Problem Solving from Nature - PPSN XIII, Ljubljana, Slovenia. doi: 10.1007/978-3-319-10762-2_14.
  • Trautmann H, Wagne T, Brockhoff D. 2013. ‘R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection.’Contributed to the Learning and Intelligent Optimization Conference 7, Catania, Italy.
  • Sosa Hernández V, Schütze O, Rudolph G, Trautmann H. 2013. ‘The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume.’ In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV, edited by Emmerich M, Deutz A, Schuetze O, Bäck T, Tantar A, Moral PD, Legrand P, Bouvry P, Coello CA, 189-205.: Springer International Publishing. doi: 10.1007/978-3-319-01128-8_13.
  • Preuss M, Kozakowski D, Hagelbäck J, Trautmann H. 2013. ‘Reactive strategy choice in StarCraft by means of Fuzzy Control.’Contributed to the 2013 IEEE Conference on Computational Inteligence in Games (CIG), Niagara Falls, ON, Canada,.
  • Trautmann H, Rudolph G, Dominguez-Medina C, Schütze O. 2013. ‘Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems.’ In EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II, edited by Schütze O, Coello Coello CA, Tantar A, Tantar E, Bouvry P, Del Moral P, Legrand P, 89-105.: Springer Berlin Heidelberg. doi: 10.1007/978-3-642-31519-0_6.
  • Dominguez-Medina C, Rudolph G, Schütze O, Trautmann H. 2013. ‘Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique.’Contributed to the 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico.
  • 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, Nr. 5-6: 319-337.
  • 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.
  • Rudolph G, Trautmann H, Sengupta S, Schütze O. 2013. ‘Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation.’ In Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Sheffield, UK, Proceedings, edited by Purshouse RC, Fleming PJ, Fonseca CM, Greco S, Shaw J, 443-458.: Springer.
  • Wagner T, Trautmann H, Brockhoff D. 2013. ‘Preference Articulation by Means of the R2 Indicator.’ In Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Sheffield, UK, Proceedings, edited by Purshouse R C, Fleming P J, Fonseca C M, Greco S, Shaw J, 81-95.: Springer.
  • 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 Foundations of Genetic Algorithms XII, forthcoming, edited by Neumann F, de Jong K, 147-160.
  • Sosa-Hernandez VA, Schütze O, Rudoph G, Trautmann H. 2013. ‘Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms.’ In Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion, 1699-1702. New York, NY, USA: ACM. doi: 10.1145/2464576.2482756.
  • 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 Learning and Intelligent Optimization - 6th International Conference, LION 6, Paris, edited by Hamadi Y, Schoenauer M, 115-129.: Springer.
  • Bischl B, Mersmann O, Trautmann H, Weihs C. 2012. ‘Resampling Methods in Model Validation.’ Evolutionary Computation Journal 20, Nr. 2: 249-275. doi: 10.1162/EVCO_a_00069.
  • Brockhoff D, Wagner T, Trautmann H. 2012. ‘On the Properties of the R2 Indicator.’ In Proc. 14th Int'l. Genetic and Evolutionary Computation Conference (GECCO '12), edited by Soule T, others, 465--472.: ACM. doi: 10.1145/2330163.2330230.
  • Bischl B, Mersmann O, Trautmann H, Preuss M. 2012. ‘Algorithm selection based on exploratory landscape analysis and cost-sensitive learning.’ In Genetic and Evolutionary Computation Conference, GECCO '12, Philadelphia, PA, USA, edited by Soule T, Moore JH, 313-320.: ACM.
  • Rudolph G, Trautmann H, Schütze O. 2012. ‘Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen.’ at-Automatisierungstechnik 60: 610-621. doi: 10.1524/auto.2012.1033.
  • Wagner T, Trautmann H, Marti L. 2011. ‘A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms.’ In Evolutionary Multi-Criterion Optimization, edited by Takahashi R, Deb K, Wanner E, Greco S, 16-30.: Springer Berlin / Heidelberg.
  • 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. New York, NY, USA: ACM.
  • 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.: IEEE Press. doi: 10.1109/ICEEE.2011.6106656.
  • 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. doi: 10.1177/0954410011414120.
  • 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.: IEEE.
  • 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, Nr. 5: 688--701. doi: 10.1109/TEVC.2010.2058119.
  • Mostaghim S, Trautmann H, Mersmann O. 2010. ‘Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities.’ In 11th International Conference on Parallel Problem Solving from Nature - PPSN XI, Proceedings, Part II, edited by Schaefer R, Cotta C, Kolodziej J, Rudolph G, 101-110.: Springer.
  • Azene YT, 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. doi: 10.1016/j.cirpj.2010.06.001.
  • 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.: IEEE.
  • Mersmann O, Trautmann H, Naujoks B, Weihs C. 2010. ‘On the Distribution of EMOA Hypervolumes.’ In Learning and Intelligent Optimization, 4th International Conference, LION 4, Venice, Italy, edited by Blum C, Battiti R, 333-337.: Springer.
  • Voss T, Trautmann H, Igel C. 2010. ‘New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization.’ In 11th International Conference on Parallel Problem Solving from Nature - PPSN XI, Proceedings, Part II, edited by Schaefer R, Cotta C, Kolodziej J, Rudolph G, 260-269.: Springer.
  • Mersmann O, Preuss M, Trautmann H. 2010. ‘Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.’ In 11th International Conference on Parallel Problem Solving from Nature - PPSN XI, Proceedings, Part I, edited by Schaefer R, Cotta C, Kolodziej J, Rudolph G, 73-82.: Springer.
  • Bischl B, Mersmann O, Trautmann H. 2010. ‘Resampling Methods in Model Validation.’ In Proceedings of the Workshop on Experimental Methods for the Assessment of Computational Systems (WEMACS 2010), Algorithm Engineering Report TR10-2-007, edited by Bartz-Beielstein T, Chiarandini M, Paquete L, Preuss M.: Department of Computer Science, TU Dortmund University.
  • Ding J, Wessing S, Trautmann H, Mehnen J, Naujoks B. 2010. ‘Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing.’ In Proceedings of the 7th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '10), edited by Teti R. Capri, Italy: Copyright C.O.C. Com. org. Conv.
  • Trautmann H, Mehnen J. 2009. ‘Statistical Methods for Improving Multi-objective Evolutionary Optimisation.’ International Journal of Computational Intelligence Research 5, Nr. 2: 72-78. doi: 10.5019/j.ijcir.2009.172.
  • 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, Nr. 4: 493--509.
  • T. Wagner H. Trautmann, Naujoks B. 2009. ‘OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing.’ In Evolutionary Multi-Criterion Optimization (EMO 2009), Lecture Notes in Computer Science (LNCS) 5467, edited by Fonseca C, Gandibleux X, 198-215.: Springer, Berlin.
  • Trautmann H, Mehnen J, Naujoks B. 2009. ‘Pareto-Dominance in Noisy Environments.’ In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, edited by Tyrrell A, 3119-3126.: IEEE Press.
  • Trautmann H, Mehnen J. 2009. ‘Preference-Based Pareto-Optimization in Certain and Noisy Environments.’ Engineering Optimization 41: 23-38. doi: 10.1080/03052150802347926.
  • Naujoks B, Trautmann H. 2009. ‘Online Convergence Detection for Multiobjective Aerodynamic Applications.’ In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, edited by Tyrrell A, 332-339.: IEEE Press.
  • Mehnen J, Trautmann H. 2008. ‘Robust Multi-objective Optimisation of Weld Bead Geometry for Additive Manufacturing.’ In Proceedings of the 6th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '08), edited by Teti R. Naples, Italy: Copyright C.O.C. Com. org. Conv.
  • Trautmann H, Ligges U, Mehnen J, Preuss M. 2008. ‘A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing.’ In Parallel Problem Solving from Nature (PPSN), edited by Rudolph G, others, 825-836.: Springer, Berlin.
  • 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, edited by Tan KC, Xu J, 2687-2694. Singapore.
  • Weihs C, Trautmann H. 2007. ‘Parallel Universes: Multi-Criteria Optimization.’ In Dagstuhl Seminar Proceedings 07181, Parallel Universes and Local Patterns, edited by Berthold MR, Morik K, Siebes A. Schloss Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum f�r Informatik (IBFI).
  • Trautmann H, Weihs C. 2006. ‘On the Distribution of the Desirability Index using Harrington's Desirability Function.’ Metrika 63, Nr. 2: 207-213. doi: 10.1007/s00184-005-0012-0.
  • Mehnen J, Trautmann H. 2006. ‘Integration of Expert's Preferences in Pareto Optimization by Desirability Function Techniques.’ In CIRP ICME '06) -- Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, edited by Teti R, 293-298. Ischia, Italy: C.O.C. Com. org. Conv. CIRP ICME '06.
  • Trautmann H. 2004. Qualitätskontrolle in der Industrie anhand von Kontrollkarten für Wünschbarkeitsindizes - Anwendungsfeld Lagerverwaltung.

Dissertationen

Kerschke, PascalAutomated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning2017


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