Publications

The following lists show my publications (peer-revied/non-peer-reviewed) in chronological order. My articles and corresponding citation counts can also be found on Google Scholar.

Peer-reviewed

  • Hellwig, M. and Finck, S. (2023).
    Joining Emission Data from Diverse Economical Activity Taxonomies with Evolution Strategies.
    In The 9th International Conference on Machine Learning, Optimization, and Data Science (LOD 2023), pages 1–15, Grasmere, Lake District, England - UK. Springer (accepted). https://doi.org/

  • Salcher, F., Finck, S., and Hellwig, M. (2023).
    Automated Process Capability Analysis for Product Quality Improvements.
    In proceedings of the 29th IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Edinburgh, UK, pages 1 – 9. IEEE (in press). https://doi.org/

  • Hutter, D., Hellwig, M., and Jatowt, A. (2023).
    Adaptive and Dynamic Scheduling for Robust Production Planning.
    The 35th Swedish AI Society’s annual workshop (SAIS), Karlskrona, Sweden (in press). https://grahn.cse.bth.se/SAIS-2023/extended_abstracts/paper_2.pdf

  • Urban, S., Moodbrugger, N., Hellwig, M., and Dobler, M. (2023).
    Sustainable Smart Services for Financial Institutions: A Framework to Support Banks in ESG Integration in the Risk Analysis Process of Loans.
    In West, S., Meierhofer, J., and Mangla, U., editors, 2023 Smart Services Summit, pages 1–8, Zurich. Springer Cham. https://doi.org/10.1007/978-3-031-36698-7_15

  • Hellwig, M. and Beyer, H.-G. (2022).
    Benchmarking epsilonMAg-ES and BP-epsilonMAg-ES on the bbob-constrained Testbed.
    In Proceedings of the Genetic and Evolutionary Computation Conference (Companion), GECCO ’22, pages 1-8, New York, NY, USA. Association for Computing Machinery (ACM). https://doi.org/10.1145/3520304.3534010

  • Suzic, B., Urban, S., Hellwig, and M., Dobler, M. (2022).
    Smart Circular Economy Value Drivers: The Role of the Financial Sector in Stimulating Smart Regional Innovation-Driven Growth.
    In West, S., Meierhofer, J., and Mangla, U., editors, 2022 Smart Services Summit, pages 1–8, Zurich. Springer Cham. https://doi.org/10.1007/978-3-030-97042-0_6

  • Hellwig, M., Finck, S., Mootz, T., Ehe, A., and Rein, F. (2022).
    NLP for Product Safety Risk Assessment: Towards consistency evaluations of human expert panels.
    In 2020 ISDA, pages 1–8, Online. Springer. https://doi.org/10.1007/978-3-030-96308-8_24

  • Hellwig, M. and Beyer, H.-G. (2020).
    A Modified Matrix Adaptation Evolution Strategy with Restarts for Constrained Real-World Problems.
    In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–8, Glasgow. IEEE. https://doi.org/10.1109/CEC48606.2020.9185566

  • Spettel, P., Beyer, H.-G., and Hellwig, M. (2019).
    Steady State Analysis of a Multi-Recombinative Meta-ES on a Conically Constrained Problem with Comparison to σSA and CSA.
    In Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA ’19, pages 43–57, New York, NY, USA. Association for Computing Machinery (ACM). https://doi.org/10.1145/3299904.3340306

  • Hellwig, M., Spettel, P., and Beyer, H.-G. (2019b).
    Comparison of Contemporary Evolutionary Algorithms on the Rotated Klee-Minty Problem.
    In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’19, pages 1879–1887, New York, NY, USA. Association for Computing Machinery (ACM). https://doi.org/10.1145/3319619.3326805

  • Hellwig, M. and Beyer, H.-G. (2019a).
    Analysis of a Meta-ES on a Conically Constrained Problem.
    In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’19, pages 673–681, New York, NY, USA. Association for Computing Machinery (ACM). https://doi.org/10.1145/3321707.3321824

  • Hellwig, M., Entner, D., Prante, T., Zăvoianu, A.-C., Schwarz, M., and Fink, K. (2019a).
    Optimization of Ascent Assembly Design Based on a Combinatorial Problem Representation.
    In Andrés-Pérez, E., González, L. M., Periaux, J., Gauger, N., Quagliarella, D., and Giannakoglou, K., editors, Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems, volume 49, pages 291–306. Springer International Publishing, Cham. https://dx.doi.org/10.1007/978-3-319-89890-2_19

  • Zăvoianu, A.-C., Saminger-Platz, S., Entner, D., Prante, T., Hellwig, M., Schwarz, M., and Fink, K. (2019).
    On the Optimization of 2D Path Network Layouts in Engineering Designs via Evolutionary Computation Techniques.
    In Andrés-Pérez, E., González, L. M., Periaux, J., Gauger, N., Quagliarella, D., and Giannakoglou, K., editors, Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems, volume 49, pages 307–322. Springer International Publishing, Cham. https://dx.doi.org/10.1007/978-3-319-89890-2_20

  • Hellwig, M. and Beyer, H.-G. (2018a).
    Benchmarking evolutionary algorithms for single objective real-valued constrained optimization: A critical review.
    Swarm and Evolutionary Computation. https://dx.doi.org/10.1016/j.swevo.2018.10.002

  • Hellwig, M. and Beyer, H.-G. (2018b).
    A Linear Constrained Optimization Benchmark for Probabilistic Search Algorithms: The Rotated Klee-Minty Problem.
    In Fagan, D., Martín-Vide, C., O’Neill, M., and Vega-Rodríguez, M. A., editors, Theory and Practice of Natural Computing, volume 11324, pages 139–151. Springer International Publishing, Cham. https://dx.doi.org/10.1007/978-3-030-04070-3_11

  • Hellwig, M. and Beyer, H.-G. (2018c).
    A Matrix Adaptation Evolution Strategy for Constrained Real-Parameter Optimization.
    In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–8, Rio de Janeiro. IEEE. https://dx.doi.org/10.1109/CEC.2018.8477950

  • Hellwig, M. and Beyer, H.-G. (2018d).
    On the steady state analysis of covariance matrix self-adaptation evolution strategies on the noisy ellipsoid model.
    Theoretical Computer Science. https://dx.doi.org/10.1016/j.tcs.2018.05.016

  • Spettel, P., Beyer, H.-G., and Hellwig, M. (2018).
    A covariance matrix self-adaptation evolution strategy for optimization under linear constraints.
    IEEE Transactions on Evolutionary Computation, pages 514–524. https://dx.doi.org/10.1109/TEVC.2018.2871944

  • Zăvoianu, A.-C., Saminger-Platz, S., Entner, D., Prante, T., Hellwig, M., Schwarz, M., and Fink, K. (2018).
    Multi-Objective Optimal Design of Variably Constrained 2D Path Network Layouts with Application to Ascent Assembly Engineering.
    Journal of Mechanical Design. https://dx.doi.org/10.1115/1.4039009

  • Beyer, H.-G. and Hellwig, M. (2017).
    Analysis of the pcCMSA-ES on the Noisy Ellipsoid Model.
    In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’17, pages 689–696, New York, NY, USA. ACM. https://dx.doi.org/10.1145/3071178.3079195

  • Hellwig, M. (2017).
    Analysis of mutation strength adaptation within evolution strategies on the ellipsoid model and methods for the treatment of fitness noise.
    PhD thesis, Ulm University. https://dx.doi.org/10.18725/OPARU-4253

  • Hellwig, M. and Beyer, H.-G. (2016a).
    Evolution Under Strong Noise: A Self-Adaptive Evolution Strategy Can Reach the Lower Performance Bound - The pcCMSA-ES.
    In Handl, J., Hart, E., Lewis, P. R.,López-Ibánez, M., Ochoa, G., and Paechter, B., editors, Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings, pages 26–36. Springer International Publishing, Cham. https://dx.doi.org/10.1007/978-3-319-45823-6_3

  • Hellwig, M. and Beyer, H.-G. (2016b).
    Mutation strength control via meta evolution strategies on the ellipsoid model.
    Theoretical Computer Science, 623:160–179. https://dx.doi.org/10.1016/j.tcs.2015.12.011

  • Beyer, H.-G. and Hellwig, M. (2016).
    The Dynamics of Cumulative Step Size Adaptation on the Ellipsoid Model.
    Evolutionary Computation, 24(1):25–57.
    https://dx.doi.org/10.1162/EVCO_a_00142

  • Hellwig, M. and Arnold, D. V. (2016).
    Comparison of Constraint-Handling Mechanisms for the (1, λ)-ES on a Simple Constrained Problem.
    Evolutionary Computation, 24(1):1–23. https://dx.doi.org/10.1162/EVCO_a_00139

  • Beyer, H.-G. and Hellwig, M. (2013).
    Controlling population size and mutation strength by meta-ES under fitness noise.
    In Neumann, F. and Jong, K. A. D., editors, Proceedings of the twelfth workshop on Foundations of genetic algorithms XII, FOGA XII, pages 11–24. ACM. https://dx.doi.org/10.1145/2460239.2460242

  • Beyer, H.-G. and Hellwig, M. (2012).
    Mutation strength control by meta-ES on the sharp ridge.
    In Soule, T. and Moore, J. H., editors, Genetic and Evolutionary Computation Conference, GECCO ’12, Philadelphia, USA, GECCO 2012, pages 305–312. ACM. https://dx.doi.org/10.1145/2330163.2330208

  • Hellwig, M. (2009).
    Multikriterielle Optimierung mittels adaptiver Blocknormen.
    Diplomarbeit im Studiengang Mathematik, Fachbereich Mathematik der Technischen Universität Dortmund, Dortmund.

Non-peer-reviewed

Invited talks

  • Hellwig, M. (2023).
    Designing & Deploying Artificial Intelligence Applications: Potential and Challenges
    http://

  • Hellwig, M. (2023).
    How to shape a more sustainable future with Artificial Intelligence?
    http://

  • Hellwig, M. (2021).
    Hill climbing towards Sesame Street: Applied Research in Computational Learning.
    Invited talk for OMICRON ACADEMY Knowledge Exchange Series, Omicron electronics GmBH, Klaus, Austria. December 16th, 2021

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