|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Author(s):||Thomson, Sarah L|
|Title:||Clarifying the Difference in Local Optima Network Sampling Algorithms|
|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|
|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|
|thomson.pdf||Fulltext - Accepted Version||911.01 kB||Adobe PDF||View/Open|
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