Learning Inherent Networks from Stochastic Search Methods
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.
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- 1.Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2005)Google Scholar
- 2.Watson, J.P.: An introduction to fitness landscape analysis and cost models for local search. In: Handbook of Metaheuristics, pp. 599–623. Springer (2010)Google Scholar
- 3.Tomassini, M., Verel, S., Ochoa, G.: Complex-network analysis of combinatorial spaces: The NK landscape case. Physical Review E 78(6), 066114 (2008)Google Scholar
- 5.Doye, J.P.K.: The network topology of a potential energy landscape: a static scale-free network. Phys. Rev. Lett. 88, 238701 (2002)Google Scholar
- 7.Daolio, F., Verel, S., Ochoa, G., Tomassini, M.: Local optima networks and the performance of iterated local search. In: GECCO, pp. 369–376. ACM (2012)Google Scholar
- 9.Kauffman, S.: The origins of order: Self organization and selection in evolution. Oxford University Press (1993)Google Scholar
- 13.Amor, H.B., Rettinger, A.: Intelligent exploration for genetic algorithms: using self-organizing maps in evolutionary computation. In: GECCO, pp. 1531–1538. ACM (2005)Google Scholar