Global Optimization of Multimodal Deceptive Functions

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


Local search algorithms operating in high-dimensional and multimodal search spaces often suffer from getting trapped in a local optima, therefore requiring many restarts. Even with multiple restarts, their search efficiency critically depends on the choice of the neighborhood structure. In this paper we propose an approach in which the need for the restarts is exploited to improve the neighborhood definitions. Namely, a graph clustering based linkage detection method is used to mine the information from several runs, in order to extract variable dependencies and update the neighborhood structure, variation operators accordingly. We show that the adaptive neighborhood structure approach enables the efficient solving of challenging global optimization problems that are both deceptive and multimodal.


Simulated Annealing Neighborhood Structure Graph Cluster Quantum Annealing Perturbation Probability 
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
  1. 1.HEC Lausanne, Quartier UNIL-Dorigny, Bâtiments InternefLausanneSwitzerland

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