Meta- and Hyper-Heuristics for Combinatorial Optimisation

with Michael Epitropakis and/or Ahmed Kheiri

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.