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Data Mining in Heuristic Search

In: Discrete Diversity and Dispersion Maximization

Author

Listed:
  • Eduardo Oliveira

    (Computing Institute)

  • Simone de Lima Martins

    (Computing Institute)

  • Alexandre Plastino

    (Computing Institute)

  • Isabel Rosseti

    (Computing Institute)

  • Geiza Cristina da Silva

    (Federal University of ABC)

Abstract

Heuristics using patterns extracted by data mining techniques have been successfully applied to several combinatorial optimization problems. This chapter presents a new hybrid heuristic to solve the maximum diversity problem, which combines a GRASP heuristic with data mining. While performing iterations of the GRASP heuristic to solve the problem, this hybrid heuristic stores the best obtained solutions in an elite set. Whenever the elite set is stable, i.e., when it is not changed for a while, the data mining technique is applied to extract patterns from it. The mined patterns represent characteristics of near-optimal solutions of the elite set, and the hybrid heuristic will use them to guide the construction of new and better solutions. The computational results show that the new data mining heuristic improved the quality of the results obtained by the original GRASP heuristic. It also improved some best-known results from the literature.

Suggested Citation

  • Eduardo Oliveira & Simone de Lima Martins & Alexandre Plastino & Isabel Rosseti & Geiza Cristina da Silva, 2023. "Data Mining in Heuristic Search," Springer Optimization and Its Applications, in: Rafael Martí & Anna Martínez-Gavara (ed.), Discrete Diversity and Dispersion Maximization, chapter 0, pages 301-321, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-38310-6_13
    DOI: 10.1007/978-3-031-38310-6_13
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