Advertisement

Evolutionary Synthesis of Micromachines Using Supervisory Multiobjective Interactive Evolutionary Computation

  • Raffi Kamalian
  • Ying Zhang
  • Hideyuki Takagi
  • Alice M. Agogino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)

Abstract

A novel method of Interactive Evolutionary Computation (IEC) for the design of microelectromechanical systems (MEMS) is presented. As the main limitation of IEC is human fatigue, an alternate implementation that requires a reduced amount of human interaction is proposed. The method is applied to a 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 for the fitness calculation used in selection. The results of a test on 13 users shows that this IEC method can produce statistically significant better MEMS resonators than fully automated non-interactive evolutionary approaches.

Keywords

User Study Human Interaction Pareto Frontier Human Evaluation Evolutionary Synthesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhou, N., Zhu, B., Agogino, A.M., Pister, K.S.J.: Evolutionary Synthesis of MEMS Micro Electronic Mechanical Systems Design, Intelligent Engineering System through Artificial Neural Networks. In: Proceedings of the Artificial Neural Networks in Engineering (ANNIE 2001), pp. 197–202 (2001)Google Scholar
  2. 2.
    Zhou, N., Agogino, A.M., Pister, K.S.J.: Automated Design Synthesis for Micro-Electro-Mechanical Systems (MEMS). In: Proceedings of ASME Design Automation Conference (2002)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman, Boston (1989)zbMATHGoogle Scholar
  4. 4.
    van Laarhoven, P.J.M., Aarts, E.H.L.: Simulated Annealing: Theory and Applications. Reidel Publishing, Dordrecht (1987)zbMATHGoogle Scholar
  5. 5.
    Zhou, N.: Simulation and Synthesis of Microelectromechanical Systems, Doctoral Thesis, UC Berkeley (2002)Google Scholar
  6. 6.
    Kamalian, R., Zhou, N., Agogino, A.M.: A Comparison of MEMS Synthesis Techniques. In: Proceedings of the 1st Pacific Rim Workshop on Transducers and Micro/Nano Technologies, Xiamen, China, pp. 239–242 (2002)Google Scholar
  7. 7.
    Kamalian, R.: Evolutionary Synthesis of MEMS, Doctoral Thesis, UC Berkeley (2004)Google Scholar
  8. 8.
    Takagi, H.: Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation. Proceedings of the IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  9. 9.
    Kamalian, R., Takagi, H., Agogino, A.M.: Optimized Design of MEMS by Evolutionary Multi-objective Optimization with Interactive Evolutionary Computation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 1030–1041. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Kamalian, R., Takagi, H., Agogino, A.M.: The Role of Constraints and Human Interaction in Evolving MEMS Designs: Microresonator Case Study. In: Proceedings of DETC 2004, ASME 2004 Design Engineering Technical Conference, Salt Lake City, UT (2004)Google Scholar
  11. 11.
    Singh, A., Minsker, B. S., Takagi, H.: Interactive Genetic Algorithms for Inverse Groundwater Modeling, American Society of Civil Engineers (ASCE) Environmental & Water Resources Institute (EWRI) World Water & Environmental Resources Congress 2005, Anchorage, AK, (2005) Google Scholar
  12. 12.
    Kamalian, R., Agogino, A.M.: Improving Evolutionary Synthesis of MEMS through Fabrication and Testing Feedback. In: IEEE SMC 2005, IEEE Conference on Systems, Man and Cybernetics (2005)Google Scholar
  13. 13.
    Antonsson, E.K., Cagan, J. (eds.): Formal Engineering Design Synthesis. Cambridge University Press, Cambridge (2001)Google Scholar
  14. 14.
    SUGAR: Simulation Research for MEMS, http://bsac.eecs.berkeley.edu/cadtools/sugar/sugar/
  15. 15.
    ANOVA: ANalysis Of VAriance between groups, http://www.physics.csbsju.edu/stats/anova.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Raffi Kamalian
    • 1
  • Ying Zhang
    • 2
  • Hideyuki Takagi
    • 1
  • Alice M. Agogino
    • 2
  1. 1.Faculty of DesignKyushu UniversityFukuokaJapan
  2. 2.BEST LabUniversity of CaliforniaBerkeleyUSA

Personalised recommendations