Current Research Projects

Our group is involved in several national and international research projects, funded by the German Research Foundation (DFG), the European Commission (EU), and the Australian Research Council (ARC). For a list of finished research projects, see here.

Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)

This project brings together theoreticians and practitioners who are working in the field of nature-inspired search and optimization heuristics. Its goal is on one hand to make theoretical findings more accessible for researchers and engineers who are concerned with applications, on the other hand to make theoreticians aware of practical problems and the kind of questions that arise in practical applications.

Bio-inspired Computing for Problems with Dynamically Changing Constraints

The aim of this project is to design bio-inspired computing methods for dynamically changing environments. Dynamic problems arise frequently in the areas of engineering, logistics, and manufacturing. Such problems are usually subject to a large set of constraints that change over time due to changes in resources. Algorithms that can deal with such dynamic changes would benefit decision-makers. The project aims to provide a foundational theory as the basis for the design of bio-inspired algorithms dealing with dynamically changing constraints and provide approaches for dealing with important industrial problems.

The Structure of Computational Learning

This project aims at using mathematical methods to identify the theoretical foundations of the capabilities and limits of computational learning. It uses a framework for learning criteria which allows for general statements, informing about many learning criteria at once.

Finding a Parking Place Quickly (Bachelor Project)

Looking for a parking place in urban areas is often time consuming and stressful. Up-to-date navigation systems are barely capable of directing the driver to a good nearby parking lot. This is mostly due to missing data about available parking places on the roadside and little research on this topic. In corporation with TomTom five students work on new algorithmic approaches for efficient on-street parking.

Theory of Swarm Algorithms and Their Effectiveness in Uncertain Environments (TOSU)

Bio-inspired swarm algorithms are well established in practice for solving optimization problems with complex constraints even in difficult domains involving uncertainty. This project analyzes theoretically swarm-based search heuristics for dynamically changing and stochastic objectives.

Analysis of Discrete Load Balancing on Heterogeneous Networks (ADLON)

Despite a large body of theoretical studies on load balancing, most results do not match the complex and heterogenous structure of the underlying network and the corresponding load balancing tasks. This project bridges the gap between the well-studied homogenous setting and the theoretically much less understood heterogenous load balancing problems that occur in practice.

Speed of Adaptation in Population Genetics and Evolutionary Computation (SAGE)

Biological evolution has produced an extraordinary diversity of organisms. Evolutionary computation has found many innovative solutions to optimisation and design problems. Both fields have studied the speed of adaptation independently, and with orthogonal approaches. This project brings together an interdisciplinary consortium from both fields to synergise these complementary approaches and to create the foundation of a unified quantitative theory describing the speed of adaptation in both biological and artificial evolution.

Parameterized Analysis of Bio-inspired Computing – From Theory to High Performing Algorithms

This project establishes the field of parameterised analysis of bio-inspired computing. It rigorously analyses features of instances of combinatorial optimisation problems and their impact on the runtime behaviour of bio-inspired computing methods such as evolutionary algorithms and ant colony optimisation.  

Average-Case Analysis of Parameterized Problems and Algorithms (ACAPA)

This project aims to analyze the average-case behavior of some known parameterized algorithms, mainly motivated by the recent encouraging experimental studies of these algorithms. Most of these studies record, compared to the worst-case analysis, a much more promising performance on real-world data.