Determining the Difficulty of Landscapes by PageRank Centrality in Local Optima Networks

  • Sebastian HerrmannEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9595)


The contribution of this study is twofold: First, we show that we can predict the performance of Iterated Local Search (ILS) in different landscapes with the help of Local Optima Networks (LONs) with escape edges. As a predictor, we use the PageRank Centrality of the global optimum. Escape edges can be extracted with lower effort than the edges used in a previous study. Second, we show that the PageRank vector of a LON can be used to predict the solution quality (average fitness) achievable by ILS in different landscapes.


Fitness landscape analysis Search difficulty PageRank centrality Local optima networks NK-landscapes 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Information Systems and Business AdministrationJohannes Gutenberg-UniversitätMainzGermany

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