Meta- and Hyper-Heuristics for Combinatorial Optimisation
Meta-heuristics, such as genetic algorithms, simulated annealing and tabu search, are now an established tool for solving hard optimisation problems. A more recent concept is that of “hyper-heuristics”, which are algorithms that seek to automate the process of selecting and/or generating meta-heuristics. Whereas meta-heuristics draw on Operational Research and Artificial Intelligence, hyper-heuristics draw on Machine Learning and Data Science.
The main aim of this research project is to develop high quality meta- and/or hyper-heuristics for combinatorial optimisation problems. The problems under consideration can include (but are not limited to) routing, cutting, packing, placement, graph theoretical, timetabling and scheduling problems. Applicants for this topic should have reasonable mathematical ability, a general interest in optimisation, and strong programming skills (e.g. C/C++, Python, Java, VBA). Experience with LaTeX is also desirable, but is not essential as training can be given.
Return to the Optimisation Research Group page.