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Inductive learning for engineering design optimization

Published online by Cambridge University Press:  27 February 2009

Mark Schwabacher ,
Thomas Ellman  and
Haym Hirsh
Mark Schwabacher
Affiliation:
Department of Computer Science, Hill Center for the Mathematical Sciences, Busch Campus, Rutgers, The State University of New Jersey, Piscataway, NJ 08855, U.S.A.
Thomas Ellman
Affiliation:
Department of Computer Science, Hill Center for the Mathematical Sciences, Busch Campus, Rutgers, The State University of New Jersey, Piscataway, NJ 08855, U.S.A.
Haym Hirsh
Affiliation:
Department of Computer Science, Hill Center for the Mathematical Sciences, Busch Campus, Rutgers, The State University of New Jersey, Piscataway, NJ 08855, U.S.A.
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Extract

We are working on using machine learning to make the numerical optimization of complex engineering designs faster and more reliable. We envision a system that learns from previous design sessions knowledge that enables it to assist the engineer in setting up and carrying out a new design optimization. We have performed initial experiments for two aspects of setting up an optimization: selecting a prototype to serve as a starting point for the optimization and selecting a reformulation of the search space. Both choices can dramatically affect the speed and the reliability of design optimization.

Type
Research Abstracts
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 179 - 180
Copyright
Copyright © Cambridge University Press 1996

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References

Ellman, T., Keane, J. & Schwabacher, M. (1992). The Rutgers CAP Project Design Associate. Technical Report CAP-TR-7, Department of Computer Science, Rutgers University, New Brunswick, NJ.Google Scholar
Quinlan, J.R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, CA.Google Scholar
Schwabacher, M., Ellman, T., Hirsh, H. & Richter, G. (1996). Learning to choose a reformulation for numerical optimization of engineering designs. Artificial Intelligence in Design Conference. Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
Schwabacher, M., Hirsh, H., & Ellman, T. (1994). Learning prototypeselection rules for case-based iterative design. Proc. Tenth IEEE Conf. Artif. Intell. Applications, 56–62.CrossRefGoogle Scholar