Abstract
This paper describes an evolutionary algorithm that was developed for catalog design. This algorithm is based on genetic algorithms, but uses an object-oriented coding scheme to represent a design, and introduces unique crossover and mutation operators. To account for the dependence of system performance on both system configuration and component selection, the evolutionary algorithm allows for simultaneous alterations of configurations and components. This new approach allows the consideration of alternate configurations and allows the configurations to evolve to make the best use of the available components. Using this evolutionary algorithm, a piping system was designed in which cooling fluid was delivered to three machines on a manufacturing floor at specified pressures and flow rates. The algorithm was able to find good designs that satisfied the given design specifications.
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References
Androulakis, I. P. and V. Venkatasubramanian, A Genetic Algorithmic Framework For Process Design and Optimization, Computers and Chemical Engineering, vol. 15, no. 4 (1991): 217–228.
Brown, D. and K-Y. Hwang, Solving Fixed Configuration Problems with Genetic Search, Research in Engineering Design, vol. 5 (1993): 80–87.
Carlson, S. Genetic Algorithm Attributes for Component Selection, Research in Engineering Design, vol. 8, no. 1 (1996): 33–51.
Carlson, S., R. Shonkwiler and M. Ingrim, Comparison of Three Non-derivative Optimization Methods with a Genetic Algorithm for Component Selection, Journal of Engineering Design, vol. 5, no. 4 (1994): 367–378.
Chawdhry, P. K., S. E. Potter, S. J. Culley and C. R. Burrows, Configuration Design with Neural Networks, ASME Design Technical Conference Proceedings, August 18–22, paper number 96-DETC/DAC-1462 (1996).
Cheremisinoff, N. P., Fluid Flow, Ann Arbor Science Publishers, Ann Arbor, MI, (1982).
Eshelman, L. J. and J. D. Schaffer, Preventing Premature Convergence in Genetic Algorithms by Preventing Incest, Proceedings of the 4th International Conference on Genetic Algorithms, Morgan Kaufman, San Mateo, CA (1991): 115–121.
Freeman, P. and A. Newell, A Model For Functional Reasoning in Design, Proceedings of the 2nd International Joint Conference on Artificial Intelligence, Detroit, MI (1971): 621–633.
Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA (1989).
Heinrich, M. and W. E. Jeungst, Resource Based Paradigm for the Configuring of Technical Systems from Modular Components, Proceedings of the 1996 ASME Design Engineering Technical Conference and Computers in Engineering Conference, Irvine, CA, August 18–22, 1996.
Holland, J., Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA (1975).
Jeppson, R. W., Analysis of Flow in Pipe Networks, Ann Arbor Science Publisher, Ann Arbor, MI (1977).
Kota, S. and C. L. Lee, General Framework For Configuration Design: Part I — Methodology, Journal of Engineering Design, vol. 4, no. 4 (1993): 277–289.
Michalewicz, Z., A Survey of Constraint Handling Techniques in Evolutionary Computation Methods, Proceedings of the Fourth Annual Conference on EvolutionaryProgramming (1995): 153–155.
Mittal, S. and F. Frayman, Towards A Generic model Of Configuration Tasks, Proceedings of the International Joint Conference on Artificial Intelligence, Detroit, MI (1989): 1395–1401.
Mironer, A., Engineering Fluid Mechanics, McGraw-Hill, New York, NY (1979).
Pham, D. T. and Y. Yang, A Genetic Algorithm Based Preliminary Design System, Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering, vol. 207, no. D2 (1993): 127–133.
Richardson, J., M. Palmer, G. Liepins and M. Hilliard, Some Guidelines for Genetic Algorithms and Penalty Functions, Proceedings of the Third International Conference on Genetic Algorithms (1989): 191–197.
Schmidt, L. and J. Cagan, Grammars for Machine Design, Artificial Intelligence in Design 1996, J. S. Gero and F. Sudweeks (eds), Kluwer Academic, Netherlands (1996): 325–344.
Snavely, G. and P. Papalambros, Abstraction as a Configuration Design Methodology, Advances in Design Automation, ASME DE-Vol. 65-1 (1995): 297–305.
Spears, W. M. and K. A. DeJong, On the Virtues of Parameterized Uniform Crossover, Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufman, San Mateo, CA (1991): 230–236.
Streeter, V. L. and E. B. Wylie, Fluid Mechanics, McGraw-Hill, New York, NY (1985).
Syswerda, G., Uniform Crossover in Genetic Algorithms, Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufman, San Mateo, CA (1989): 2–9.
White, F. M., Fluid Mechanics, McGraw-Hill, New York, NY (1986).
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Carlson-Skalak, S., White, M.D. & Teng, Y. Using an evolutionary algorithm for catalog design. Research in Engineering Design 10, 63–83 (1998). https://doi.org/10.1007/BF01616688
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DOI: https://doi.org/10.1007/BF01616688