Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34557
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Thomson, Sarah L
Ochoa, Gabriela
Verel, Sébastien
Contact Email: s.l.thomson@stir.ac.uk
Title: Fractal Dimension and Perturbation Strength: A Local Optima Networks View
Editor(s): Rudolph, Günter
Kononova, Anna V.
Aguirre, Hernán
Kerschke, Pascal
Ochoa, Gabriela
Tušar, Tea
Citation: Thomson SL, Ochoa G & Verel S (2022) Fractal Dimension and Perturbation Strength: A Local Optima Networks View. In: Parallel Problem Solving from Nature. Lecture Notes in Computer Science, 13398. Parallel Problem Solving from Nature – PPSN XVII 17th International Conference, PPSN 2022, Dortmund, Germany, 10.09.2022-14.09.2022. Cham, Switzerland: Springer, pp. 562-574. https://doi.org/10.1007/978-3-031-14714-2_39
Issue Date: 2022
Date Deposited: 16-Sep-2022
Series/Report no.: Lecture Notes in Computer Science, 13398
Conference Name: Parallel Problem Solving from Nature – PPSN XVII 17th International Conference, PPSN 2022
Conference Dates: 2022-09-10 - 2022-09-14
Conference Location: Dortmund, Germany
Abstract: 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 are graphs mapping algorithm search connectivity in a landscape. LONs are constructed for QAP instances and fractal dimension measurements taken from the networks. Thereafter, the interplay between perturbation strength, LON fractal dimension, and algorithm difficulty on the underlying combina-torial problems is analysed. The results show that higher-perturbation LONs also have higher fractal dimensions. ILS algorithm performance prediction using fractal dimension features may benefit more from LONs formed using a high perturbation strength; this model configuration enjoyed excellent performance. Around half of variance in Robust Taboo Search performance on the data-set used could be explained with the aid of fractal dimension features.
Status: AM - Accepted Manuscript
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is a post-peer-review, pre-copyedit version of a paper published in Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13398. Springer, Cham, pp. 532-574. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-14714-2_39
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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