Reduced Human Fatigue Interactive Evolutionary Computation for Micromachine Design
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).
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.
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