Reduced Human Fatigue Interactive Evolutionary Computation for Micromachine Design
Inhaltsverzeichnis
Reference
Raffi R. Kamalian, Ying Zhang, Hideyuki Takagi, and Alice M. Agogino: Reduced Human Fatigue Interactive Evolutionary Computation for Micromachine Design. 4th International Conference on Machine Learning and Cybernetics (ICMLC 2005), Guangzhou, China, pp.5666-5671 (August 18-21, 2005).
DOI
http://dx.doi.org/10.1109/ICMLC.2005.1527946
Abstract
This paper presents a novel method of using interactive evolutionary computation (IEC) for the design of microelectromechanical systems (MEMS). A key limitation of IEC is human fatigue. Based on the results of a study of a previous IEC MEMS tool, an alternate form that requires less human interaction is presented. The method is applied on top of a conventional multi-objective genetic algorithm, with the human in a supervisory role, providing evaluation only every n th-generation. Human interaction is applied to the evolution process by means of Pareto-rank shifting, which is used for the fitness calculation used in selection. Results of a test of 13 users shows that this IEC method can produce statistically significant better MEMS resonators than non-interactive evolutionary synthesis.
Extended Abstract
Bibtex
Used References
[1] N. Zhou, B. Zhu, A. M. Agogino, K.S.J. Pister,, “Evolutionary Synthesis of MEMS MicroElectronicMechanical Systems Design,” Proceedings of the Artificial Neural Networks in Engineering (ANNIE2001), pp.197-202, 2001.
[2] N. Zhou, A. M. Agogino, "Automated Design Synthesis for Micro-Electro-Mechanical Systems (MEMS)", Proceedings of the ASME Design Automation Conference, Montreal, Canada, Sept. 2002.
[3] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989.
[4] P. J. M. van Laarhoven, E. H. L. Aarts, Simulated Annealing: Theory and Applications, Reidel Publishing Company, Dordrecht, Holland, 1987.
[5] N. Zhou, “Simulation and Synthesis of Microelectromechanical Systems”, Doctoral thesis, UC Berkeley, 2002.
[6] R. Kamalian, N. Zhou, A. M. Agogino, "A Comparison of MEMS Synthesis Techniques", Proceedings of the 1st Pacific Rim Workshop on Transducers and Micro/Nano Technologies, Xiamen, China, pp. 239-242, July 2002.
[7] R. Kamalian, “Evolutionary Synthesis of MEMS”, Doctoral thesis, UC Berkeley, 2004.
[8] H. Takagi, “Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation”, Proceedings of the IEEE, vol. 89, no. 9, pp. 1275-1296, 2001.
[9] R. Kamalian, H. Takagi and A. M. Agogino, “Optimized Design of MEMS by Evolutionary Multi-objective Optimization with Interactive Evolutionary Computation,” Proceedings of GECCO 2004, Genetic and Evolutionary Computation Conference, Seattle, pp. 1030-1041, June 2004.
[10] R. Kamalian, A. M. Agogino, and H. Takagi, “The Role Of Constraints and Human Interaction in Evolving MEMS Designs: Microresonator Case Study,” Proceedings of DETC'04, ASME 2004 Design Engineering Technical Conference, Salt Lake City, UT, October 2004.
[11] R. Kamalian, A. M. Agogino, “Improving Evolutionary Synthesis of MEMS through Fabrication and Testing Feedback”, submitted to SMC 2005, IEEE Conference on Systems, Man and Cybernetics, Hawaii, October 2005.
[12] E. K. Antonsson, J. Cagan, eds., Formal Engineering Design Synthesis, Cambridge University Press, Cambridge, 2001.
[13] SUGAR, Simulation Research for MEMS, http://bsac.eecs.berkeley.edu/cadtools/sugar/sugar/
[14] ANOVA: ‘ANalysis Of VAriance between groups’, http://www.physics.csbsju.edu/stats/anova.html.
Links
Full Text
http://best.berkeley.edu/pubs/05_0402_P.pdf
