Florian Siegmund

My research interest are nature-inspired, heuristic optimization algorithms for simulation-based optimization and decision support, and their strategies to handle uncertainty in the simulation models. I have obtained a doctoral degree in operations research/industrial informatics from the School of Engineering Science at University of Skövde in Sweden. I received my diploma degree in computer science from Karlsruhe Institute of Technology in Germany. My PhD supervisors were Prof. Kalyanmoy Deb from Michigan State University, USA and Prof. Amos Ng from University of Skövde.

Journal articles

• Amos H.C. Ng, Florian Siegmund, and Kalyanmoy Deb. "Reference Point Based Evolutionary Multi-Objective Optimization with Dynamic Resampling for Production Systems Improvement". Complete manuscript, to be submitted to International Journal of Production Economics.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb. "Standard Error Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization". Submitted to Computers & Operations Research. COIN Report No. 2015021.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb. "Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization of Stochastic Systems". Submitted to European Journal of Operations Research. COIN Report No. 2015020.

Conference Papers

• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2017. "A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization". In Proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, March 2017, Münster, Germany, pp. 560-574, ISBN 978-3-319-54157-0, DOI: 10.1007/978-3-319-54157-0_38.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2016. "A Ranking and Selection Strategy for Preference-based Evolutionary Multi-objective Optimization of Variable-Noise Problems". In Proceedings of the Congress on Evolutionary Computation WCCI-CEC 2016, July 2016, Vancouver, Canada, pp. 3035-3044, ISBN 978-1-5090-0623-6, DOI: 10.1109/CEC.2016.7744173.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2016. "Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-objective Optimization of Invariant-Noise Problems". In Proceedings of the Evostar-EvoApplications 2016 conference, March 2016, Porto, Portugal, pp. 311-326, ISBN 978-3-319-31152-4. DOI: 10.1007/978-3-319-31153-1_21.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2015. "Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization". In Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, March 2015, Guimarães, Portugal, pp. 366-380, ISBN 978-3-319-15934-8. DOI: 10.1007/978-3-319-15934-8.
• Kalyanmoy Deb, Florian Siegmund, and Amos H.C. Ng, 2014. "R-HV: A Metric for Computing Hyper-volume for Reference Point-based EMOs". In Proceedings of the International Conference on Swarm, Evolutionary, and Memetic Computing, December 2014, Bhubaneswar, Odisha, India, pp. 98-110, ISBN 978-3-319-20293-8. DOI: 10.1007/978-3-319-20294-5_9.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2013. "A Comparative Study of Dynamic Resampling Strategies for Guided Evolutionary Multi-Objective Optimization". In Proceedings of 2013 IEEE Congress on Evolutionary Computation, June 2013, Cancún, Mexico, pp. 1826-1835, ISBN 978-1-4799-0454-9. DOI: 10.1109/CEC.2013.6557782.
• Florian Siegmund, Jacob Bernedixen, Leif Pehrsson, Amos H.C. Ng, and Kalyanmoy Deb, 2012. "Reference Point-based Evolutionary Multi-objective Optimization for Industrial Systems Simulation", In Proceedings of Winter Simulation Conference 2012, Dec 2012, Berlin, Germany, ISBN 978-1-4673-4781-5. DOI: 10.1109/WSC.2012.6465130.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2012. "Finding a preferred diverse set of Pareto-optimal solutions for a limited number of function calls", In Proceedings of 2012 IEEE World Congress on Computational Intelligence - Congress on Evolutionary Computation, June 2012, Brisbane, Australia, pp. 2417-2424, ISBN 978-1-4673-1508-1. DOI: 10.1109/CEC.2012.6256654.

Theses

• Florian Siegmund, 2016. "Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization of Stochastic Systems", PhD thesis at University of Skövde, Sweden, Nov 2016, ISBN 978-91-982690-1-7. [Download]
  Article about the thesis: Swedish, English
• Florian Siegmund, 2010. "Optimal Computing Budget Allocation for Noisy Hypervolume-Based Multi-Objective Evolutionary Optimization", Master thesis at Karlsruhe Institute of Technology, Germany and National University of Singapore, Nov 2010.
• Florian Siegmund, 2009. "Sequential Sampling in Noisy Multi-Objective Evolutionary Optimization", Master thesis at University of Skövde, Sweden and Karlsruhe Institute of Technology, Germany, Sept 2009. [Download]

Talks

• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2015. "Dynamic Resampling for Preference-based Evolutionary Multi-Objective Optimization of Stochastic Systems", International Conference on Multiple Criteria Decision Making 2015, August 2015, Hamburg, Germany.
• Florian Siegmund, Amos H.C. Ng, and Kalyanmoy Deb, 2013. "Adaptive Guided Evolutionary Multi-Objective Optimization", International Conference on Multiple Criteria Decision Making 2013, June 2013, Málaga, Spain.
• Florian Siegmund, Amos H.C. Ng, Alexander Karlsson, and Kalyanmoy Deb, 2013. "Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization of Stochastic Systems", International Conference on Multiple Criteria Decision Making 2013, June 2013, Málaga, Spain.

ResearcherID: F-6692-2010, ORCID: 0000-0003-3432-5068, Google scholar profile