Willkommen auf den Webseiten der MALEO Gruppe

Die Gruppe Maschinelles Lernen und Optimierung (MALEO) unter der Leitung von Prof. Dr. Heike Trautmann wurde im Oktober 2023 an der Universität Paderborn gegründet. Unsere theoretische und empirische Forschung konzentriert sich auf (vertrauenswürdige) Künstliche Intelligenz, Data Science, (automatisiertes) maschinelles Lernen, automatisierte Algorithmenauswahl und -konfiguration, explorative Landschaftsanalyse, Analyse von randomisierten Suchheuristiken und (mehrkriterielle) evolutionäre Optimierung. In interdisziplinärer Zusammenarbeit befassen wir uns darüber hinaus mit Themen der Computational Social Sciences wie Social Influence Analysis und Disinformation Campaign Detection in Online-Medien. Wir haben vielfältige und umfangreiche nationale sowie internationale Kooperationen mit besonders starken Verbindungen zur Universität Twente, Enschede. Auf europäischer Ebene unterstützen wir die Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) und das European Research Center for Information Systems (ERCIS).

Für weitere Information verweisen wir auf die offizielle Website unserer Gruppe.


  • Vorlesung: Unsupervised Learning and Evolutionary Optimisation Using R 
  • Seminar: Automated Algorithm Selection 
  • Vorlesung: Angewandte Statistik mit R
    Lecturer: Prof. Dr. Heike Trautmann

    Die Vorlesung gibt einen Überblick über sowohl deskriptive Statistik und explorative Datenanalyse als auch induktive Statistik und Grundlagen der Wahrscheinlichkeitsrechnung. Der Schwerpunkt liegt neben der Vermittlung von intuitivem Verständnis auf der angewandten Datenanalyse unter Zuhilfenahme der statistischen Programmiersprache R, die im ersten Block der Vorlesung im Rahmen eines Programmierkurses eingeführt wird, entsprechende Vorkenntnisse sind hier nicht erforderlich.
  • VorlesungMulti-Objective Optimisation 
    Lecturer: Dr. Jakob Bossek

    Optimization problems are ubiquitous, and we all (approximately) solve them in everyday life, such as when finding routes with Google Maps to quickly get from point A to point B or deciding on a checkout lane with the shortest waiting queue (shortest expected waiting time) at the supermarket. However, optimization problems are rarely single-criteria. Instead, they are typically multi-criteria in nature, with the individual objectives usually conflicting with each other. For example, in route planning, the distance traveled may be relevant (shorter is better), and fuel consumption may also be a consideration (lower is better). The shortest route may lead through the city center with many stop-and-go maneuvers at red lights, especially during peak hours. On the other hand, a longer route around the city may consume less fuel. Accordingly, the goal in multi-objective optimization is to find a set of optimal compromise solutions.

    This course provides a comprehensive introduction to multi-objective optimization and the associated challenges. In addition to classical general approaches, exact methods for selected combinatorial optimization problems are presented, along with heuristic (nature-inspired) methods. The course also covers heuristic solution approaches for problems with more than three criteria (many-objective optimization).
  • Seminar: Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization 
    Lecturer: Prof. Dr. Heike Trautmann

    This seminar, partly based on an upcoming respective book by Saxena, Deb et al. (2024), focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMO). EMO algorithms, namely EMOAs, iteratively evolve a set of solutions towards an optimal trade-off surface, i.e. Pareto Front. The availability of multiple solution sets over successive generations makes EMOAs amenable to application of ML for different pursuits, a large proportion of optimization problems in practice is multi-objective in nature.

    We will cover the foundations of optimization, including problem and algorithm types and then focus on key studies on ML-based enhancements in the EMO domain, systematically addressing important aspects. Moreover, we will discuss Auto-ML based techniques such as Automated EMOA configuration. The seminar will have two phases, theoretical will be followed by experimental work in groups.
  • Seminar: Data Stream Mining
    Lecturer: Prof. Dr. Heike Trautmann

    This seminar will focus on the characteristics of Data Streams and related Data Stream Mining algorithms. We will mainly concentrate on textual data in the social networks domain and discuss application areas such as Disinformation Campaign Detection or Synthetic Social Network Data Generation. The seminar will have two phases, theoretical will be followed by experimental work in groups.
  • Proseminar: Evolutionary Computation
    Lecturer: Dr. Jakob Bossek

    The key idea of this seminar is to dive into diverse topics in the context of evolutionary computation. Evolutionary algorithms (EAs) are bio-inspired randomized search heuristics that mimic principles from natural evolution to “evolve” good solutions iteratively. Such algorithms particularly shine in the context of finding good solutions for black-box optimization problems (where knowledge about the function at hand is obtainable via queries only) or (multi-objective) NP-hard combinatorial optimisation problems
  • Project Group: Next Gen User Interface for optimization software based on Generative AI (GenUIne)
    Lecturer: Dr. Moritz Seiler, Prof. Dr. Heike Trautmann

    Using generative AI we are going to re-invent the next gen User Interface for optimization software in close cooperation with the company Optano (https://optano.com).

    Optimization software (e.g. https://optano.com/en/solutions/) has a wide range of use cases and features, it is hard to learn and complex to use, especially when users are no domain experts or rarely use such tools. For those users the power of optimization remains a locked secret.

    This project will demonstrate how latest generative AI, e.g. LLMs, can support users to understand complex software products, get insight in their data and run complex tasks automatically and without deep knowledge of the tool. Users will interact with optimization software using their mother tongue. We will create a great experience for users. We leverage GenAI. We are GenUIne!


Do Additional Target Points Speed Up Evolutionary Algorithms?
J. Bossek, D. Sudholt, Theoretical Computer Science (2023) 113757.
A study on the effects of normalized TSP features for automated algorithm selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145.
Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features
R.P. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke, H. Trautmann, O. Mersmann, in: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2023, pp. 129–139.
Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features
R.P. Prager, H. Trautmann, in: J. Correia, S. Smith, R. Qaddoura (Eds.), Applications of Evolutionary Computation, Springer Nature Switzerland, Cham, 2023, pp. 411–425.
Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets
L. Schäpermeier, P. Kerschke, C. Grimme, H. Trautmann, in: M. Emmerich, A. Deutz, H. Wang, A.V. Kononova, B. Naujoks, K. Li, K. Miettinen, I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization, Springer Nature Switzerland, Cham, 2023, pp. 291–304.
On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp. 248–256.
Runtime Analysis of Quality Diversity Algorithms
J. Bossek, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp. 1546–1554.
Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space
A. Marrero, E. Segredo, E. Hart, J. Bossek, A. Neumann, in: Proceedings of the Genetic} and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2023, pp. 312–320.
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Marion Hucke

Machine Learning and Optimisation

Raum FU.316
Universität Paderborn
Fürstenallee 11
33102 Paderborn