Wel­come to the MA­LEO group

The Machine Learning and Optimisation (MALEO) group, led by Prof. Dr. Heike Trautmann, was established in October 2023 at Paderborn University, Germany. Our theoretical and empirical research mainly focuses on (Trustworthy) Artificial Intelligence, Data Science, (Automated) Machine Learning, Automated Algorithm Selection and Configuration, Exploratory Landscape Analysis, Analysis of Randomised Search Heuristics, and (Multiobjective) Evolutionary Optimisation. In interdisciplinary collaboration, we are moreover addressing Computational Social Science topics such as Social Influence Analysis and Disinformation Campaign Detection in Open Online Media. We have diverse and extensive national and international collaborations with particularly strong links to the University of Twente, Enschede. At the European level, we strongly support the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) and the European Research Center for Information Systems (ERCIS).

We refer the reader to the official MALEO-group research-website for more information.

Re­cent and Past Teach­ing

  • Lecture: Angewandte Statistik mit R
    Lecturer: Prof. Dr. Heike Trautmann

    The lecture provides an overview of descriptive statistics and exploratory data analysis as well as inductive statistics and the basics of probability theory. In addition to conveying an intuitive understanding of respective methods, a further focus is on applied data analysis making use of the statistical programming language R, which is introduced in the first block of the lecture as part of a programming course. No prior knowledge of R is required.
     
  • Lecture: Multi-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!

  • Lecture: Unsupervised Learning and Evolutionary Optimisation Using R 
  • Seminar: Automated Algorithm Selection 

Planned Lec­tures

The following lectures, seminars and project groups are planned for the coming semesters. Please note, however, that the details are provisional and may be subject to change! 

Planned Lec­tures

  • Lecture: Fundamental Algorithms  (Bachelor elective, English, 3V + 2Ü)
  • Lecture: Unsupervised Learning and Evolutionary Computation Using R (Master, English, 3V + 2Ü)
  • Project Group: (Master, English) Next Gen User Interface for optimization software based on Generative AI (GenUIne)
  • Lecture: Multi-Objective Optimisation (Master, English, 3V + 2Ü)

Pub­lic­a­tions

2024

Exploratory Landscape Analysis for Mixed-Variable Problems

@article{Prager_Trautmann_2024, title={Exploratory Landscape Analysis for Mixed-Variable Problems}, DOI={10.1109/TEVC.2024.3399560}, journal={IEEE Transactions on Evolutionary Computation}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2024}, pages={1–1} }


Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization

@inproceedings{Dietrich_Prager_Doerr_Trautmann_2024, place={Cham}, title={Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization}, booktitle={Parallel Problem Solving from Nature — PPSN XVIII}, publisher={Springer International Publishing}, author={Dietrich, K. and Prager, R. and Doerr, C. and Trautmann, Heike}, editor={Affenzeller, M and Winkler, S and Kononova, A and Trautmann, H and Tušar, T and Machado, P and Baeck, T}, year={2024}, pages={1–14} }


Learned Features vs. Classical ELA on Affine BBOB Functions

@inproceedings{Seiler_Skvorc_Cenikj_Doerr_Trautmann_2024, place={Cham}, title={Learned Features vs. Classical ELA on Affine BBOB Functions}, booktitle={Parallel Problem Solving from Nature — PPSN XVIII}, publisher={Springer International Publishing}, author={Seiler, Moritz and Skvorc, Urban and Cenikj, G and Doerr, C and Trautmann, Heike}, editor={Affenzeller, M and Winkler, S and Kononova, A and Trautmann, H and Tušar, T and Machado, P and Baeck, T}, year={2024}, pages={1–14} }


Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP

@inproceedings{Seiler_Rook_Heins_Preuß_Bossek_Trautmann_2024, title={Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}, DOI={10.1109/ssci52147.2023.10372008}, booktitle={2023 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher={IEEE}, author={Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}, year={2024} }


On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems

@inbook{Preuß_Rook_Trautmann_2024, place={Cham}, title={On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems}, DOI={10.1007/978-3-031-56852-7_20}, booktitle={Applications of Evolutionary Computation}, publisher={Springer Nature Switzerland}, author={Preuß, Oliver Ludger and Rook, Jeroen and Trautmann, Heike}, year={2024} }


2023

Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python

@article{Prager_Trautmann_2023, title={Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}, DOI={10.1162/evco_a_00341}, journal={Evolutionary Computation}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2023}, pages={1–25} }


Do Additional Target Points Speed Up Evolutionary Algorithms?

@article{Bossek_Sudholt_2023, title={Do Additional Target Points Speed Up Evolutionary Algorithms?}, DOI={10.1016/j.tcs.2023.113757}, journal={Theoretical Computer Science}, author={Bossek, Jakob and Sudholt, Dirk}, year={2023}, pages={113757} }


On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem

@article{Bossek_Grimme_2023, title={On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem}, DOI={10.1162/evco_a_00335}, journal={Evolutionary Computation}, author={Bossek, Jakob and Grimme, Christian}, year={2023}, pages={1–35} }


A study on the effects of normalized TSP features for automated algorithm selection

@article{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2023, title={A study on the effects of normalized TSP features for automated algorithm selection}, volume={940}, DOI={https://doi.org/10.1016/j.tcs.2022.10.019}, journal={Theoretical Computer Science}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2023}, pages={123–145} }


Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features

@inproceedings{Prager_Dietrich_Schneider_Schäpermeier_Bischl_Kerschke_Trautmann_Mersmann_2023, place={New York, NY, USA}, series={FOGA ’23}, title={Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features}, DOI={10.1145/3594805.3607136}, booktitle={Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Prager, Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier, Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann, Olaf}, year={2023}, pages={129–139}, collection={FOGA ’23} }


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Con­tact

Marion Hucke

Machine Learning and Optimisation

Room FU.316
Paderborn University
Fürstenallee 11
33102 Paderborn

Office hours

Monday afternoon
Tuesday morning
Thursday afternoon