Department of Electrical Engineering, Mechanical Engineering and Technical Journalism

Alexander Hagg (DE)

Dr Alexander Hagg

Post-Doktorand/Künstliche Intelligenz (Surrogate Modeling)/Künstliche Intelligenz (Optimization)

alexander.hagg@h-brs.de

Unit

Dr Alexander Hagg

Research fields

  • Computer Aided Ideation, Computer Aided Intuition
  • Optimization, insb. evolutionary algorithms, quality diversity, phenotypic niching
  • Surrogatmodellierung und maschinellem Lernen, insb. Gaußprocessregression, neuronale Netze, Neuroevolution
  • Computer Vision
  • Robotics

Location

Sankt Augustin

Address

Grantham-Allee 20

53757 Sankt Augustin

Research Projects

Full Domain Analysis for Fluid Mechanics

Artificial intelligence methods can efficiently help us understand aftermath requirements, constraints, and decision-making processes at an early stage. Algorithms are typically used in late stages of engineering development projects. We want to reverse this and show engineers early on which types of solutions meet their requirements.

Project management at the H-BRS

Dr Alexander Hagg
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Publications

  • Hagg, A., 2021. Discovering the preference hypervolume: an interactive model for real world computational co-creativity (Doctoral dissertation, Leiden University).
  • Hagg, A., 2021. Phenotypic Niching Using Quality Diversity Algorithms. In Metaheuristics for Finding Multiple Solutions (pp. 287-315). Springer, Cham.
  • Hagg, A., Preuss, M., Asteroth, A. and Bäck, T., 2020. An Analysis of Phenotypic Diversity in Multi-Solution Optimization (No. 3286). EasyChair.
  • Hagg, A., Wilde, D., Asteroth, A. and Bäck, T., 2020. Designing Air Flow with Surrogate-assisted Phenotypic Niching.
  • Asteroth, A., Hagg, A., Meng, J., Priesnitz, A., Prochnau, L. and Reith, D., 2020. AErOmAt Abschlussbericht.
  • Hagg, A., Zaefferer, M., Stork, J. and Gaier, A., 2019, July. Prediction of neural network performance by phenotypic modeling. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1576-1582).
  • Hagg, A., Asteroth, A. and Bäck, T., 2019, July. Modeling user selection in quality diversity. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 116-124).
  • Hagg, A., Asteroth, A., Bäck, T. Prototype Discovery using Quality-Diversity (PPSN 2018)
  • Hagg, A. Hierarchical Surrogate Modeling for Illumination Algorithms. (GECCO 2017).
  • Hagg, A., Mensing M., Asteroth A. Evolving Parsimonious Networks by Mixing Activation Functions. (GECCO 2017).
  • Spieker H., Hagg, A., Gaier, A., Meilinger, S., Asteroth, A. Multi-stage evolution of single-and multiobjective MCLP. (Soft Computing 2016).
  • Hagg, A., Hegger, F., Plöger, P. (2016). On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities. (RoboCup International Symposium 2016).
  • Hagg, A., Spieker, H., Oslislo, A., Jacobs, V., Asteroth, A. and Meilinger, S., 2015. Methodische Grundlegung für eine Strategie zum sukzessiven Ausbau der Ladeinfrastruktur für Elektromobilität in Bonn und dem Rhein-Sieg-Kreis.
  • Asteroth, A., Hagg, A. How to successfully apply genetic algorithms in practice: Representation and parametrization. (INISTA 2015).
  • Spieker, H., Hagg, A., Asteroth, A., Meilinger, S., Jacobs, V., Oslislo, A. Successive evolution of charging station placement. (INISTA 2015).
  • Dwiputra, R., Füller, M., Hegger, F., Schneider, S., Hochgeschwender, N., Awaad, I., Loza, J.M.S., Ozhigov, A.Y., Biswas, S., Deshpande, N.V. and Hagg, A., The b-it-bots RoCKIn@ Work 2014 Team Description Paper.
  • Dwiputra, R., Füller, M., Hegger, F., Schneider, S., Hochgeschwender, N., Awaad, I., Loza, J.M.S., Ozhigov, A.Y., Biswas, S., Deshpande, N.V. and Hagg, A., 2014. The b-it-bots Robo-Cup@ Home 2014 Team Description Paper. Joao Pessoa, Brazil.