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

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2016)

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

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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.

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Notes

  1. 1.

    For the reader’s convenience, we wanted this paper to be self-contained. In the introductory sections, we included descriptions and formal definitions for Fitness Landscapes and PageRank following the explanations in [9].

  2. 2.

    We have also replicated this result to predict the average fitness achieved by local search with LONs with basin transition probabilities. Results are available from the authors upon request.

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Authors and Affiliations

  1. Department of Information Systems and Business Administration, Johannes Gutenberg-Universität, Jakob-Welder-Weg 9, 55128, Mainz, Germany

    Sebastian Herrmann

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  1. Sebastian Herrmann
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Correspondence to Sebastian Herrmann .

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Editors and Affiliations

  1. University of Málaga, Málaga, Spain

    Francisco Chicano

  2. Austrian Insttitute of Technology, Vienna, Austria

    Bin Hu

  3. University of Granada, Granada, Spain

    Pablo García-Sánchez

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Herrmann, S. (2016). Determining the Difficulty of Landscapes by PageRank Centrality in Local Optima Networks. In: Chicano, F., Hu, B., García-Sánchez, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2016. Lecture Notes in Computer Science(), vol 9595. Springer, Cham. https://doi.org/10.1007/978-3-319-30698-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-30698-8_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30697-1

  • Online ISBN: 978-3-319-30698-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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