@Conference{EURO2022,
  author    = {Daniel Karapetyan and Jack Warren and Ender \"Ozcan and Andrew J. Parkes},
  title     = {Automated Algorithm Configuration for the Quadratic Unconstrained Binary Optimisation Problem},
  year      = {2022},
  publisher = {EURO 2022, Espoo, Finland},
  abstract  = {Quadratic Unconstrained Binary Optimisation (QUBO) is a mathematical model that can conveniently represent many combinatorial optimisation problems such as number partitioning, max-cut problem, graph colouring, etc. It is of specific interest as it is the representation used within the "D-Wave Quantum System" for solving optimisation problems. Here, we use the Conditional Markov Chain Search (CMCS) metaheuristic framework to automatically build a metaheuristic combining several low-level heuristics (mutations and local search operators). CMCS is a highly-configurable framework that is flexible enough to model a range of standard and new metaheuristics, yet its behaviour is completely controlled by numerical parameters. We designed an algorithm configuration method to optimise the CMCS configuration. It is a hybrid metaheuristic built specifically for CMCS configuration. The CMCS configuration obtained by our algorithm configuration method performs well across benchmarks and achieves state-of-the-art results on the widely used random benchmark instances.},
  keywords  = {Metaheuristics, Artificial Intelligence, Programming, Quadratic},
}