Data Mining in Heuristics

Marcelo R. H. Maia (), Isabel Rosseti (), Simone de Lima Martins () and Alexandre Plastino ()
Additional contact information
Marcelo R. H. Maia: Universidade Federal Fluminense
Isabel Rosseti: Universidade Federal Fluminense
Simone de Lima Martins: Universidade Federal Fluminense
Alexandre Plastino: Universidade Federal Fluminense

Chapter 3 in Handbook of Heuristics, 2025, pp 41-69 from Springer

Abstract: Abstract This chapter explores some heuristics incorporating data mining procedures. 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 the subsequent 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 heuristics may benefit from data mining by obtaining better solutions in shorter computational times.

Keywords: Data mining; Frequent patterns; Hybrid metaheuristics; Problem decomposition; Problem size reduction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-00385-0_11

Ordering information: This item can be ordered from
http://www.springer.com/9783032003850

DOI: 10.1007/978-3-032-00385-0_11

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().