A genetic algorithm for continuous design space search

Artificial Intelligence in Engineering

Volumn 11, Issue 3 SPEC. ISS., 1997, Pages 295-305

  Rasheed, Khaled ^a   Hirsh, Haym ^a   Gelsey, Andrew ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Design optimization; Genetic algorithms

Indexed keywords

BINARY CODES; GENETIC ALGORITHMS; OPTIMIZATION; STRUCTURAL DESIGN; SUPERSONIC AIRCRAFT;

DESIGN SPACE SEARCH;

COMPUTER AIDED DESIGN;

EID: 0031185503     PISSN: 09541810     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0954-1810(96)00050-7     Document Type: Article

References (22)

1. Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989.
2. Wright, A., Genetic algorithms for real parameter optimization. In The First Workshop on the Foundations of Genetic Algorithms and Classifier Systems, Indiana University, Bloomington, Morgan Kaufmann, July 1990, pp. 205-18.
3. Janikow, C. & Michalewicz, Z., An experimental comparison of binary and floating point representations in genetic algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, 1991, pp. 31-6.
4. Gelsey, A., Schwabacher, M. & Smith, D., Using modeling knowledge to guide design space research. In Fourth International Conference on Artificial Intelligence in Design '96, 1996 (in press).
5. Lawrence, C., Zhou, J. and Tits, A., User's guide for CFSQP version 2.3: A C code for solving (large scale) constrained nonlinear (minimax) optimization problems, generating iterates satisfying all inequality constraints. Technical Report TR-94-16r1, Institute for Systems Research, University of Maryland, August 1995.
6. Zha, G.-C., Smith, D., Schwabacher, M., Rasheed, K., Gelsey, A. & Knight, D., High performance supersonic missile inlet design using automated optimization. In AIAA Symposium on Multidisciplinary Analysis and Optimization '96, 1996.
7. Haas, M., Elmquist, R. & Sobel, D., NAWC inlet design and analysis (NIDA) code. Final Report, UTRC Report R92-970037-1, 1992.
8. Gill, P.E., Murray, W. & Wright, M. H., Practical Optimization. Academic Press, London, New York, 1981.
9. Vanderplaats, G. N., Numerical Optimization Techniques for Engineering Design: With Applications. McGraw-Hill, New York, 1984.
10. Peressini, A. L., Sullivan, F. E. & Uhl, J. J. Jr., The Mathematics of Nonlinear Programming. Springer-Verlag, New York, 1988.
11. Moré, J.J. & Wright, S.J., Optimization Software Guide. SIAM, Philadelphia, PA, 1993.
12. Papalambros, P. & Wilde, J., Principles of Optimal Design. Cambridge University Press, New York, 1988.
13. Siu Shing Tong, Powell, D. & Goel, S., Integration of artificial intelligence and numerical optimization techniques for the design of complex aerospace systems. In 1992 Aerospace Design Conference, Irvine, CA, February 1992, AIAA-92-1189.
14. Powell, D., Inter-GEN: A hybrid approach to engineering design optimization. Technical report, Rensselaer Polytechnic Institute Department of Computer Science, December 1990, PhD thesis.
15. Powell, D. & Skolnick, M., Using genetic algorithms in engineering design optimization with non-linear constraints. In Proceedings of the Fifth International Conference on Genetic Algorithms. University of Illinois at Urbana-Champaign, Morgan Kaufmann, July 1993, pp. 424-31.
16. Bouchard, E.E., Kidwell, G. H. & Rogan, J. E., The application of artificial intelligence technology to aeronautical system design. In AIAA/AHS/ASEE Aircraft Design Systems and Operations Meeting, Atlanta, Georgia, September 1988, AIAA-88-4426.
17. Bouchard, E. E., Concepts for a future aircraft design environment. In 1992 Aerospace Design Conference, Irvine, CA, February 1992, AIAA-92-1188.
18. Sobieszczanski-Sobieski, J., James, B. B. & Dovi, A. R., Structural optimization by multilevel decomposition. AIAA Journal,1995, 23(11), 1775-82.
19. Agogino, A. M. & Almgren, A. S., Techniques for integrating qualitative reasoning and symbolic computing. Engineering Optimization, 1987, 12, 117-35.
20. Williams, B. C. & Cagan, J., Activity analysis: the qualitative analysis of stationary points for optimal reasoning. In Proceedings, 12th National Conference on Artificial Intelligence, Seattle, Washington, August 1994, pp. 1217-23.
21. Hoeltzel, D. & Chieng, W., Statistical machine learning for the cognitive selection of nonlinear programming algorithms in engineering design optimization. In Advances in Design Automation, Boston, MA, 1987.
22. Cerbone, G., Machine learning in engineering: Techniques to speed up numerical optimization. Technical Report 92-30-09, Oregon State University Department of Computer Science, 1992, PhD thesis.