Multi-objective evolutionary computation aims to find high quality (Pareto optimal) solutions that represent the trade-off between multiple objectives.

Promotors: Prof.dr. T.H.W. Bäck, Prof.dr. B. Sendhoff (Technische Universität Darmstadt)

For optimization problems, it is often unclear how to choose the most appropriate optimization algorithm. As such, rigorous benchmarking practices are critical to ensure we can gain as much insight into the strengths and weaknesses of these types of algorithms.

This thesis involves three topics: benchmarking discrete optimization algorithms, empirical analyses of evolutionary computation, and automatic algorithm configuration.

Thomas Bäck wins the 2015 IEEE CIS Evolutionary Computation Pioneer Award for his contributions in synthesizing evolutionary computation.

This thesis investigates how quantum, quantum-inspired, and hybrid quantum-classical computation can enhance key points of the automated machine learning (AutoML) pipeline under the constraints of noisy intermediate-scale quantum (NISQ) devices.