Generation and Classification of Structural Topologies With Genetic Algorithm Speciation
Author:
Duda J. W.1, Jakiela M. J.1
Affiliation:
1. Department of Mechanical Engineering, Computer-Aided Design Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
Abstract
Extending previous efforts, this article describes how a speciating genetic algorithm is used to distribute subsets of the evolving population of solutions over the design space. This distribution of solutions is analogous to different species exploiting different niches in an ecosystem. In addition to reviewing genetic algorithms with an emphasis on techniques to cause such niche exploitation, we describe how we use statistical cluster analysis techniques to quantify the extent to which a population is speciated and how this measure can be used to probabilistically encourage mating of reasonably similar designs (i.e., intraspecies mating). Results demonstrate the creation of different good designs of characteristically different topology and shape.
Publisher
ASME International
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials
Reference8 articles.
1. Chapman C. , and JakielaM., 1996, “Genetic Algorithm-Based Structural Topology Design with Compliance and Topology Simplification Considerations,” ASME JOURNAL OF MECHANICAL DESIGN, Vol. 118, No. 1, pps. 89–98. 2. Chapman C. , SaitouK., and JakielaM., 1994, “Genetic Algorithms as an Approach to Configuration and Topology Design,” ASME JOURNAL OF MECHANICAL DESIGN, Vol. 116, No. 4, pp. 1005–1012. 3. Chapman, C., Saitou, K., Jakiela, M., 1993, “Genetic Algorithms as an Approach to Configuration and Topology Design,” Proceedings of the ASME 19th Design Automation Conference: Advances in Design Automation, Volume 1, American Society of Mechanical Engineers, DE-Volume 65-1, New York, pps. 485–498. 4. Deb, K., and Goldberg, D., 1989, “An Investigation of Niche and Species Formation in Genetic Function Optimization,” Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Inc., San Mateo California, pp. 42–50. 5. Goldberg, D., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Massachusetts.
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