In this area, we consider the design of algorithms for classical combinatorial optimization problems. An important area of research are submodular optimization problems as well as multi-component problems such as the traveling thief problem.
We study foundations of bio-inspired computation techniques such as evolutionary algorithms and ant colony optimization. Obtaining rigorous insights into these highly successful methods for complex optimization problems increases their theoretical understanding and provides guidelines for the design of even more powerful algorithms. Our new bio-inspired computing techniques are based on theorectical insights and constitute reliable and efficient methods for dealing with complex problems.
In this area, we design and analyze algorithms for complex real-world problems from important areas such as mining and health.
Renewable energy is playing an increasing role in the supply of energy worldwide and will help mitigate climate change.
We as computer scientists contribute to the mitigation with the development of algorithmic solutions to a wide range of challenges.
For example, we have extensive experience in the design optimisation of wind farms, and in the development of
human-readable prediction models.