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

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4 Author(s)
S. P. Coy ; Coll. of Eng. & Appl. Sci., Arizona State Univ., Tempe, AZ, USA ; B. L. Golden ; G. C. Runger ; E. A. Wasil

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

Published in:

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans  (Volume:28 ,  Issue: 4 )