Summary: Z. Zeng & J. Wang (Eds.): Adv. in Neural Network Research & Appli., LNEE 67, pp. 211218.
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Interactive Hybrid Evolutionary Computation for MEMS
and Alice M. Agogino2
School of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA
Department of Mechanical Engineering, University of California at Berkeley, CA, USA
Abstract. An interactive hybrid evolutionary computation (IHC) process for
MEMS design synthesis is described, which uses both human expertise and
local performance improvement to augment the performance of an evolutionary
process. The human expertise identifies good design patterns, and local
optimization fine-tunes these designs so that they reach their potential at early
stages of the evolutionary process. At the same time, the feedback on local
optimal designs confirms and refines the human assessment. The advantages of
the IHC process are demonstrated with micromachined resonator test cases.