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20 August 1992 Inductive learning in engineering: a case study
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Giuseppe Cerbone and Thomas G. Diettirich "Inductive learning in engineering: a case study", Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); https://doi.org/10.1117/12.139961
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