All Outputs (3)

Universally Hard Hamiltonian Cycle Problem Instances (2022)
Presentation / Conference Contribution
Sleegers, J., Thomson, S. L., & van den Berg, D. (2022). Universally Hard Hamiltonian Cycle Problem Instances. In T. Bäck, B. van Stein, C. Wagner, J. Garibaldi, H. Lam, M. Cottrell, …J. Kacprzyk (Eds.), Proceedings of the 14th International Joint Conf

In 2021, evolutionary algorithms found the hardest-known yes and no instances for the Hamiltonian cycle problem. These instances, which show regularity patterns, require a very high number of recursions for the best exact backtracking algorithm (Vand... Read More about Universally Hard Hamiltonian Cycle Problem Instances.

Fractal Dimension and Perturbation Strength: A Local Optima Networks View (2022)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., & Verel, S. (2022). Fractal Dimension and Perturbation Strength: A Local Optima Networks View. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN

We study the effect of varying perturbation strength on the fractal dimensions of Quadratic Assignment Problem (QAP) fitness landscapes induced by iterated local search (ILS). Fitness landscapes are represented as Local Optima Networks (LONs), which... Read More about Fractal Dimension and Perturbation Strength: A Local Optima Networks View.

On funnel depths and acceptance criteria in stochastic local search (2022)
Presentation / Conference Contribution
Thomson, S. L., & Ochoa, G. (2022). On funnel depths and acceptance criteria in stochastic local search. In GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference (287-295). https://doi.org/10.1145/3512290.3528831

We propose looking at the phenomenon of fitness landscape funnels in terms of their depth. In particular, we examine how the depth of funnels in Local Optima Networks (LONs) of benchmark Quadratic Assignment Problem instances relate to metaheuristic... Read More about On funnel depths and acceptance criteria in stochastic local search.