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)


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.


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.


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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

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