Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-24T16:24:38.285Z Has data issue: false hasContentIssue false
  • English
  • Français

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.
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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