The objective of this paper is to demonstrate the application of genetic algorithms to the engine technology selection process. The “technology identification, evaluation, and selection” method is discussed in conjunction with genetic algorithm optimization as a technique to quickly evaluate the impact of various technologies and select the subset with the highest potential payoff. Techniques used to model various aspects of engine technologies are described, with emphasis on technology constraints and their impact on the combinatorial optimization of technologies. Challenges include objective function formulation and development of models to deal with incompatibilities among different technologies. Typical results are presented for an 80-technology optimization using various visualization techniques to assist in easy interpretation of genetic algorithm results. Finally, several ideas for future development of these methods are briefly explored.

1.
Kirby, M. R., and Mavris, D. N., 2001, “A Technique for Selecting Emerging Technologies for a Fleet of Commercial Aircraft to Maximize R&D Investment,” Paper No. SAE2001-01-3018.
2.
Mavris, D. N., Kirby, M. R., and Qiu, S., 1998, “Technology Impact Forecasting for a High Speed Civil Transport,” Paper No. SAE-985547.
3.
Kirby, M. R., 2001, “A Method for Technology Identification, Evaluation and Selection in Conceptual and Preliminary Aircraft Design,” Ph.D. thesis, Georgia Institute of Technology, Atlanta, GA.
4.
Roth, B., German, B. J., Mavris, D. N., and Macsotai, N., 2001, “Adaptive Selection of Engine Technology Solution Sets From a Large Combinatorial Space,” Paper No. AIAA2001-3208.
5.
Kauffman, S., 1995, At Home in the Universe, Oxford University Press, New York, p. 56.
6.
Roth, B. A., Ender, T., and Mavris, D. N., 2002, “Technology Portfolio Assessments Using a Modified Genetic Algorithm Approach,” Paper No. AIAA2002-5424.
7.
Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
8.
Khuri, S., Back, Th., and Heitkotter, J., 1994, “An Evolutionary Approach to Combinatorial Optimization Problems,” Proc. of the 22nd Annual ACM Computer Science Conference, D. Cizmar, ed., ACM Press, New York, pp. 66–73.
9.
Muhlenbein, H., 1992, “How Genetic Algorithms Really Work: I. Mutation and Hill Climbing,” R. Manner and B. Manderick, eds., Parallel Problem Solving From Nature, 2, Elsevier, Amsterdam, pp. 15–25.
10.
Back, Th., and Schutz, M., 1996, “Intelligent Mutation Rate Control in Canonical Genetic Algorithms,” Foundations of Intelligent Systems, 9th International Symposium ISMIS ’96 (Volume Lecture Notes in Artificial Intelligence 1079), Springer-Verlag, New York, pp. 158–167.
11.
Back, Th., 1992, “The Interaction of Mutation Rate, Selection and Self Adaptation Within a Genetic Algorithm,” Parallel Problem Solving From Nature, 2, R. Manner and B. Manderick, eds., Elsevier, Amsterdam, pp. 85–94.
12.
Roth, B. A., Mavris, D. N., Graham, M. D., and Macsotai, N. I., 2002, “Adaptive Selection of Pareto-Optimal Engine Technology Solution Sets,” 23rd Congress of the International Aeronautical Sciences, Toronto, Sept. 8–13.
13.
Horn, J., Nafpliotis, N., and Goldberg, D. E., 1994, “A Niched Pareto Genetic Algorithm for Multiobjective Optimization,” Proceedings of the First IEEE Conference of Evolutionary Computation, Orlando, FL. IEEE, Piscataway, NJ.
14.
Fonseca, C. M., and Fleming, P. J., 1993, “Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization,” Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan-Kauffman, San Mateo, CA.
You do not currently have access to this content.