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

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9595)

Abstract

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.

Keywords

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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