The Use of Artificial Intelligence to Improve the Numerical
Optimization of Complex Engineering Designs
Mark Schwabacher
Ph.D. Thesis
Rutgers University
Department of Computer Science
October, 1996
Thesis Committee:
Thomas Ellman, Rutgers CS
Andrew Gelsey, Rutgers CS
Haym Hirsh, Rutgers CS
Doyle Knight, Rutgers Department of Mechanical and Aerospace Engineering
Gradient-based numerical optimization of complex engineering designs
promises to produce better designs rapidly. However, such
methods generally assume that the objective function and constraint
functions are continuous, smooth, and defined everywhere.
Unfortunately, realistic simulators tend to violate these assumptions.
We present several artificial intelligence-based techniques for
improving the numerical optimization of complex engineering designs in
the presence of such pathologies in the simulators. We have tested the
resulting system in several realistic engineering domains, and have found
that using our techniques can greatly decrease the cost of design space
search, and can also increase the quality of the resulting designs.
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Mark Schwabacher