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.
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.
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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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
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