Knowledge-Based Evolutionary Linkage in MEMS Design Synthesis

  • Corie L. Cobb
  • Ying Zhang
  • Alice M. Agogino
  • Jennifer Mangold
Part of the Studies in Computational Intelligence book series (SCI, volume 157)


Multi-objective Genetic Algorithms (MOGA) and Case-based Reasoning (CBR) have proven successful in the design of MEMS (Micro-electro-mechanical Systems) suspension systems. This work focuses on CBR, a knowledge-based algorithm, and MOGA to examine how biological analogs that exist between our evolutionary system and nature can be leveraged to produce new promising MEMS designs. Object-oriented data structures of primitive and complex genetic algorithm (GA) elements, using a component-based genotype representation, have been developed to restrict genetic operations to produce feasible design combinations as required by physical limitations or practical constraints. Through the utilization of this data structure, virtual linkage between genes and chromosomes are coded into the properties of pre-defined GA objects. The design challenge involves selecting the right primitive elements, associated data structures, and linkage information that promise to produce the best gene pool for new functional requirements. Our MEMS synthesis framework, with the integration of MOGA and CBR algorithms, deals with the linkage problem by integrating a component-based genotype representation with a CBR automated knowledge-base inspired by biomimetic ontology. Biomimetics is proposed as a means to examine and classify functional requirements so that case-based reasoning algorithms can be used to map design requirements to promising initial conceptual designs and appropriate GA primitives. CBR provides MOGA with good linkage information through past MEMS design cases while MOGA inherits that linkage information through our component-bsased genotype representation. A MEMS resonator test case is used to demonstrate this methodology.


Bilateral Symmetry Stiffness Ratio Full Symmetry Comb Drive Constraint Case 
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 2008

Authors and Affiliations

  • Corie L. Cobb
    • 1
  • Ying Zhang
    • 2
  • Alice M. Agogino
    • 1
  • Jennifer Mangold
    • 1
  1. 1.Mechanical Engineering DepartmentUniversity of CaliforniaBerkeleyUSA
  2. 2.School of Electrical and Computer EngineeringGeorgia Institute of TechnologySavannahUSA

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