The Chair covers the algorithmic analysis of general-purpose optimisation paradigms that draw inspiration from biological systems. Evolutionary algorithms mimic Darwinian principles such as survival of the fittest to artificially evolve candidate solutions for optimisation and design problems. Swarm intelligence paradigms such as ant colony optimisation or particle swarm optimisation are based on the collective intelligence of animal swarms.
We work on providing a theoretical foundation for understanding the working principles of these heuristic algorithms through quantifying how quickly they find satisfactory solutions for various problems. This exposes how performance depends on algorithmic parameters and design choices, and helps to design better bio-inspired optimisation algorithms.