Artificial Intelligence for Engineering Design, Analysis and Manufacturing


Hierarchical component-based representations for evolving microelectromechanical systems designs

Ying Zhanga1 and Alice M. Agoginoa2

a1 School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, Georgia, USA

a2 Department of Mechanical Engineering, University of California, Berkeley, Berkeley, California, USA


In this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The genetic algorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the genetic algorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.

(Received July 07 2008)

(Accepted February 11 2010)

(Online publication October 07 2010)


  • Component-Based Genotype Representation;
  • Microelectromechanical System Design;
  • Multiobjective Genetic Algorithm


Reprint requests to: Ying Zhang, School of Electrical and Computer Engineering, Georgia Institute of Technology, 210 Technology Circle, Savannah, GA 31407, USA. E-mail: