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Proceedings Paper

Soft computing and metaheuristics: using knowledge and reasoning to control search and vice-versa
Author(s): Piero P. Bonissone
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Paper Abstract

Meta-heuristics are heuristic procedures used to tune, control, guide, allocate computational resources or reason about object-level problem solvers in order to improve their quality, performance, or efficiency. Offline meta-heuristics define the best structural and/or parametric configurations for the object-level model, while on-line heuristics generate run-time corrections for the behavior of the same object-level solvers. Soft Computing is a framework in which we encode domain knowledge to develop such meta-heuristics. We explore the use of meta-heuristics in three application areas: a) control; b) optimization; and c) classification. In the context of control problems, we describe the use of evolutionary algorithms to perform offline parametric tuning of fuzzy controllers, and the use of fuzzy supervisory controllers to perform on-line mode-selection and output interpolation. In the area of optimization, we illustrate the application of fuzzy controllers to manage the transition from exploration to exploitation of evolutionary algorithms that solve the optimization problem. In the context of discrete classification problems, we have leveraged evolutionary algorithms to tune knowledge-based classifiers and maximize their coverage and accuracy.

Paper Details

Date Published: 30 December 2003
PDF: 17 pages
Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); doi: 10.1117/12.512627
Show Author Affiliations
Piero P. Bonissone, GE Global Research Ctr. (United States)

Published in SPIE Proceedings Vol. 5200:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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