|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||PageRank centrality for performance prediction: the impact of the local optima network model|
|Keywords:||Fitness landscape analysis|
Local optima networks
|Citation:||Herrmann S, Ochoa G & Rothlauf F (2018) PageRank centrality for performance prediction: the impact of the local optima network model. Journal of Heuristics, 24 (3), pp. 243-264. https://doi.org/10.1007/s10732-017-9333-1|
|Abstract:||A local optima network (LON) compresses relevant features of fitness landscapes in a complex network, where nodes are local optima and edges represent transition probabilities between different basins of attraction. Previous work has found that the PageRank centrality of local optima can be used to predict the success rate and average fitness achieved by local search based metaheuristics. Results are available for LONs where edges describe either basin transition probabilities or escape edges. This paper studies the interplay between the type of LON edges and the ability of the PageRank centrality for the resulting LON to predict the performance of local search based metaheuristics. It finds that LONs are stochastic models of the search heuristic. Thus, to achieve an accurate prediction, the definition of the LON edges must properly reflect the type of diversification steps used in the metaheuristic. LONs with edges representing basin transition probabilities capture well the diversification mechanism of simulated annealing which sometimes also accepts worse solutions that allow the search process to pass between basins. In contrast, LONs with escape edges capture well the diversification step of iterated local search, which escapes from local optima by applying a larger perturbation step.|
|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. The final publication is available at Springer via http://dx.doi.org/10.1007/s10732-017-9333-1|
|pagerank_joh2017.pdf||Fulltext - Accepted Version||13.8 MB||Adobe PDF||View/Open|
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