Abstract
A Local Optima Network represents fitness landscape connectivity within the space of local optima as a mathematical graph. In certain other complex networks or graphs there have been recent observations made about inherent self-similarity. An object is said to be self-similar if it shows the same patterns when measured at different scales; another word used to convey self-similarity is fractal. The fractal dimension of an object captures how the detail observed changes with the scale at which it is measured, with a high fractal dimension being associated with complexity. We conduct a detailed study on the fractal nature of the local optima networks of a benchmark combinatorial optimisation problem (NK Landscapes). The results draw connections between fractal characteristics and performance by three prominent metaheuristics: Iterated Local Search, Simulated Annealing, and Tabu Search.
The original version of this chapter was revised: Figure 4 was corrected. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-77449-7_13
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Acknowledgements
This work is supported by the Leverhulme Trust (award number RPG-2015-395) and by the UK’s Engineering and Physical Sciences Research Council (grant number EP/J017515/1). We gratefully acknowledge that all network data used during this research were obtained from [2].
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Thomson, S.L., Verel, S., Ochoa, G., Veerapen, N., McMenemy, P. (2018). On the Fractal Nature of Local Optima Networks. In: Liefooghe, A., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018. Lecture Notes in Computer Science(), vol 10782. Springer, Cham. https://doi.org/10.1007/978-3-319-77449-7_2
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