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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kamalian, R., Zhang, Y., Takagi, H., Agogino, A.M. (2006). Evolutionary Synthesis of Micromachines Using Supervisory Multiobjective Interactive Evolutionary Computation. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_45
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DOI: https://doi.org/10.1007/11739685_45
Publisher Name: Springer, Berlin, Heidelberg
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