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Data Mining in Stochastic Local Search

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Abstract

This chapter explores some stochastic local search heuristics that incorporate a data mining procedure. The basic idea of using data mining inside a heuristic is to obtain knowledge from previous iterations performed by a heuristic to guide the search in next iterations. Patterns extracted from good quality solutions can be used to guide the search, leading to a more effective exploration of the solution space. This survey shows that memoryless heuristics may benefit from the use of data mining by obtaining better solutions in smaller computational times. Also, some results are revisited to demonstrate that even memory-based heuristics can benefit from using data mining by reducing the computational time to achieve good quality solutions.

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Author information

Authors and Affiliations

  1. Instituto de Ciência da Computaçào, Niterói, Rio de Janeiro, Brazil

    Simone de Lima Martins, Isabel Rosseti & Alexandre Plastino

Authors
  1. Simone de Lima Martins
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  2. Isabel Rosseti
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  3. Alexandre Plastino
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Corresponding author

Correspondence to Simone de Lima Martins .

Editor information

Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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de Lima Martins, S., Rosseti, I., Plastino, A. (2018). Data Mining in Stochastic Local Search. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_11

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