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Proceedings Paper

Inductive learning in engineering: a case study
Author(s): Giuseppe Cerbone; Thomas G. Diettirich
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Paper Abstract

This paper applies learning techniques to make engineering optimization more efficient and reliable. When the function to be optimized is highly non-linear, the search space generally forms several disjoint convex regions. Unless gradient-descent search is begun in the right region, the solution found will be suboptimal. This paper formalizes the task of learning effective search control for choosing which regions to explore to find a solution close to the global optimum. It defines a utility function for measuring the quality of search control. The paper defines and experimentally compares three algorithms that seek to find search control knowledge of maximum utility. The best algorithm, UTILITYID3, gives a speedup of 4.4 over full search (of all convex regions) while sacrificing only 5% in average solution quality.

Paper Details

Date Published: 20 August 1992
PDF: 9 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139961
Show Author Affiliations
Giuseppe Cerbone, Oregon State Univ. (United States)
Thomas G. Diettirich, Oregon State Univ. (United States)

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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