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International Conference on Learning and Intelligent Optimization

LION 2011: Learning and Intelligent Optimization pp 106–115Cite as

An Optimal Stopping Strategy for Online Calibration in Local Search

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6683))

Abstract

This paper formalizes the problem of choosing online the number of explorations in a local search algorithm as a last-success problem. In this family of stochastic problems the events of interest belong to two categories (success or failure) and the objective consists in predicting when the last success will take place. The application to a local search setting is immediate if we identify the success with the detection of a new local optimum. Being able to predict when the last optimum will be found allows a computational gain by reducing the amount of iterations carried out in the neighborhood of the current solution. The paper proposes a new algorithm for online calibration of the number of iterations during exploration and assesses it with a set of continuous optimisation tasks.

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References

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Author information

Authors and Affiliations

  1. Machine Learning Group, Département d’Informatique, Faculté des Sciences, ULB, Université Libre de Bruxelles, 1050, Bruxelles, Belgium

    Gianluca Bontempi

Authors
  1. Gianluca Bontempi
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Editors and Affiliations

  1. Departmento de Computation, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. IPN No. 2508, Col. San Pedro Zacatenco, D.F. 0360, Mexico, Mexico

    Carlos A. Coello Coello

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© 2011 Springer-Verlag Berlin Heidelberg

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Bontempi, G. (2011). An Optimal Stopping Strategy for Online Calibration in Local Search. In: Coello, C.A.C. (eds) Learning and Intelligent Optimization. LION 2011. Lecture Notes in Computer Science, vol 6683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25566-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-25566-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25565-6

  • Online ISBN: 978-3-642-25566-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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