Learning Inherent Networks from Stochastic Search Methods

  • David Iclănzan
  • Fabio Daolio
  • Marco Tomassini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8600)


Analysis and modeling of search heuristics operating on complex problems is a difficult albeit important research area. Inherent networks, i.e. the graphs whose vertices represent local optima and the edges describe the weighted transition probabilities between them, enable a network characterization of combinatorial fitness landscapes. Methods revealing such inherent structures of the search spaces in relation to deterministic move operators, have been recently developed for small problem instances. This work proposes a more general, scalable, data-driven approach, that extracts the transition probabilities from actual runs of metaheuristics, capturing the effect and interplay of a broader spectrum of factors. Using the case of N K landscapes, we show that such an unsupervised learning approach is successful in quickly providing a coherent view of the inherent network of a problem instance.


Search Space Local Optimum Problem Instance Adjacency Matrix Search Heuristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • David Iclănzan
    • 1
  • Fabio Daolio
    • 1
  • Marco Tomassini
    • 1
  1. 1.Faculty of Business and EconomicsUniversity of LausanneSwitzerland

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