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Using computational learning strategies as a tool for combinatorial optimization

Using computational learning strategies as a tool for combinatorial optimization In this paper, we describe how a basic strategy from computational learning theory can be used to attack a class of NP‐hard combinatorial optimization problems. It turns out that the learning strategy can be used as an iterative booster: given a solution to the combinatorial problem, we will start an efficient simulation of a learning algorithm which has a “good chance” to output an improved solution. This boosting technique is a new and surprisingly simple application of an existing learning strategy. It yields a novel heuristic approach to attack NP‐hard optimization problems. It does not apply to each combinatorial problem, but we are able to exactly formalize some sufficient conditions. The new technique applies, for instance, to the problems of minimizing a deterministic finite automaton relative to a given domain, the analogous problem for ordered binary decision diagrams, and to graph coloring. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Using computational learning strategies as a tool for combinatorial optimization

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References (30)

Publisher
Springer Journals
Copyright
Copyright © 1998 by Kluwer Academic Publishers
Subject
Computer Science; Computer Science, general; Artificial Intelligence (incl. Robotics); Mathematics, general; Complexity
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1023/A:1018991519323
Publisher site
See Article on Publisher Site

Abstract

In this paper, we describe how a basic strategy from computational learning theory can be used to attack a class of NP‐hard combinatorial optimization problems. It turns out that the learning strategy can be used as an iterative booster: given a solution to the combinatorial problem, we will start an efficient simulation of a learning algorithm which has a “good chance” to output an improved solution. This boosting technique is a new and surprisingly simple application of an existing learning strategy. It yields a novel heuristic approach to attack NP‐hard optimization problems. It does not apply to each combinatorial problem, but we are able to exactly formalize some sufficient conditions. The new technique applies, for instance, to the problems of minimizing a deterministic finite automaton relative to a given domain, the analogous problem for ordered binary decision diagrams, and to graph coloring.

Journal

Annals of Mathematics and Artificial Intelligence – Springer Journals

Published: Oct 4, 2004

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