Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29137
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: Clarifying the Difference in Local Optima Network Sampling Algorithms
Editor(s): Liefooghe, A
Paquete, L
Citation: Thomson SL, Ochoa G & Verel S (2019) Clarifying the Difference in Local Optima Network Sampling Algorithms. In: Liefooghe A & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, 11452. The 19th European Conference on Evolutionary Computation in Combinatorial Optimisation, Leipzig, Germany, 24.04.2019-26.04.2019. Cham, Switzerland: Springer International Publishing, pp. 163-178. https://doi.org/10.1007/978-3-030-16711-0_11
Issue Date: 2019
Series/Report no.: Lecture Notes in Computer Science, 11452
Conference Name: The 19th European Conference on Evolutionary Computation in Combinatorial Optimisation
Conference Dates: 2019-04-24 - 2019-04-26
Conference Location: Leipzig, Germany
Abstract: We conduct the first ever statistical comparison between two Local Optima Network (LON) sampling algorithms. These methodologies attempt to capture the connectivity in the local optima space of a fitness landscape. One sampling algorithm is based on a random-walk snowballing procedure, while the other is centred around multiple traced runs of an Iterated Local Search. Both of these are proposed for the Quadratic Assignment Problem (QAP), making this the focus of our study. It is important to note the sampling algorithm frameworks could easily be modified for other domains. In our study descriptive statistics for the obtained search space samples are contrasted and commented on. The LON features are also used in linear mixed models and random forest regression for predicting heuristic optimisation performance of two prominent heuristics for the QAP on the underlying combinatorial problems. The model results are then used to make deductions about the sampling algorithms’ utility. We also propose a specific set of LON metrics for use in future predictive models alongside previously-proposed network metrics, demonstrating the payoff in doing so.
Status: AM - Accepted Manuscript
Rights: This is a post-peer-review, pre-copyedit version of an article published in Liefooghe A & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, 11452. The 19th European Conference on Evolutionary Computation in Combinatorial Optimisation, Leipzig, Germany, 24.04.2019-26.04.2019. Cham, Switzerland: Springer International Publishing, pp. 163-178. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-16711-0_11

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