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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

3. Data Mining in Stochastic Local Search

verfasst von : Simone de Lima Martins, Isabel Rosseti, Alexandre Plastino

Erschienen in: Handbook of Heuristics

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Biased Random-Key Genetic Progamming
Nächstes Kapitel Evolution Strategies
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Metadaten
Titel
Data Mining in Stochastic Local Search
verfasst von
Simone de Lima Martins
Isabel Rosseti
Alexandre Plastino
Copyright-Jahr
2018
Buch
Handbook of Heuristics

Print ISBN: 978-3-319-07123-7

Electronic ISBN: 978-3-319-07124-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-07124-4_11

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