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Inhaltsverzeichnis
Forschungsinteressen
- Genetic Programming (GP)
- Program Synthesis
- Machine Learning
- Deep Learning
Lehre
- Seit WS 2020/21 "Entwicklung betrieblicher Informationssysteme" (FB03)
- Seit SS 2020 "Intelligent Information Systems" (FB03)
Lebenslauf
- Seit 03/2020: Wissenschaftlicher Mitarbeiter am Lehrstuhl für Wirtschaftsinformatik
- 2017 - 2020: Master of Science in Management (JGU Mainz)
- 2014 - 2017: Bachelor of Science in Wirtschaftswissenschaften (JGU Mainz)
Veröffentlichungen
2024
Boldi, R., Bao, A., Briesch, M., Helmuth, T., Sobania, D., Spector, L., & Lalejini, A. (2024). A Comprehensive Analysis of Down-sampling for Genetic Programming-based Program Synthesis. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 487-490. DOI
Boldi, R., Briesch, M., Sobania, D., Lalejini, A., Helmuth, T., Rothlauf, F., Ofria, C., & Spector, L. (2024). Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving. EVOLUTIONARY COMPUTATION, 32(4), 307-337. DOI
Briesch, M., Boldi, R., Sobania, D., Lalejini, A., Helmuth, T., Rothlauf, F., Ofria, C., & Spector, L. (2024). Improving Lexicase Selection with Informed Down-Sampling. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 25-26. DOI
Sobania, D., Petke, J., Briesch, M., & Rothlauf, F. (2024). A Comparison of Large Language Models and Genetic Programming for Program Synthesis. IEEE Transactions on Evolutionary Computation, 1-1. DOI
2023
Boldi, R., Bao, A., Briesch, M., Helmuth, T., Sobania, D., Spector, L., & Lalejini, A. (2023). The Problem Solving Benefits of Down-sampling Vary by Selection Scheme. PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION, 527-530. DOI
Briesch, M., Sobania, D., & Rothlauf, F. (2023). On the Trade-Off between Population Size and Number of Generations in GP for Program Synthesis. PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION, 535-538. DOI
Briesch, M., Sobania, D., & Rothlauf, F. (2023). Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop. Author/Publisher URL
Huschens, M., Briesch, M., Sobania, D., & Rothlauf, F. (2023). Do You Trust ChatGPT? - Perceived Credibility of Human and AI-Generated Content.
Kuhl, E., Zang, C., Esper, J., Riechelmann, D. F. C., Buentgen, U., Briesch, M., Reinig, F., Roemer, P., Konter, O., Schmidhalter, M., & Hartl, C. (2023). Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers. ECOSPHERE, 14(3). DOI
Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023). An Analysis of the Automatic Bug Fixing Performance of ChatGPT. 2023 IEEE/ACM INTERNATIONAL WORKSHOP ON AUTOMATED PROGRAM REPAIR, APR, 23-30. DOI
Sobania, D., Briesch, M., Roechner, P., & Rothlauf, F. (2023). MTGP: Combining Metamorphic Testing and Genetic Programming. DOI
2022
Briesch, M., Sobania, D., & Rothlauf, F. (2022). The Randomness of Input Data Spaces is an A Priori Predictor for Generalization (Bde. 13404, S. 17-30). DOI
Sobania, D., Briesch, M., & Rothlauf, F. (2022). Choose Your Programming Copilot A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming. PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO’22), 1019-1027. DOI
Sobania, D., Briesch, M., Wittenberg, D., & Rothlauf, F. (2022). Analyzing Optimized Constants in Genetic Programming on a Real-World Regression Problem. PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 606-607. DOI