See the forest before the trees: Fine-tuned learning and its application to the traveling salesman problem

Steven P. Coy, Bruce L. Golden, George Runger, Edward A. Wasil

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

In this paper, we introduce the concept of fine-tuned learning which relies on the notion of data approximation followed by sequential data refinement. We seek to determine whether fine-tuned learning is a viable approach to use when trying to solve combinatorial optimization problems. In particular, we conduct an extensive computational experiment to study the performance of fine-tuned-learning-based heuristics for the traveling salesman problem (TSP). We provide important insight that reveals how fine-tuned learning works and why it works well, and conclude that it is a meritorious concept that deserves serious consideration by researchers solving difficult problems.

Original languageEnglish (US)
Pages (from-to)454-464
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.
Volume28
Issue number4
DOIs
StatePublished - 1998

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'See the forest before the trees: Fine-tuned learning and its application to the traveling salesman problem'. Together they form a unique fingerprint.

Cite this