Skip to main content
×
Home
    • Aa
    • Aa
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 1
  • Cited by
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Papakonstantinou, Nikolaos Sierla, Seppo Charitoudi, Konstantinia O'Halloran, Bryan Karhela, Tommi Vyatkin, Valeriy and Turner, Irem 2014. Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA). p. 1.

    ×
  • Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Volume 25, Issue 1
  • February 2011, pp. 41-55

Hierarchical component-based representations for evolving microelectromechanical systems designs

  • Ying Zhang (a1) and Alice M. Agogino (a2)
  • DOI: http://dx.doi.org/10.1017/S0890060410000168
  • Published online: 07 October 2010
Abstract
Abstract

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.

Copyright
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

Z. Fan , J. Wang , S. Achiche , E. Goodman , & R. Rosenberg (2008). Structured synthesis of MEMS using evolutionary approaches. Applied Soft Computing Journal 8(1), 579589.

B. Lee , & K. Saitou (2007). Assembly synthesis with subassembly partitioning for optimal in-process dimensional adjustability. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(1), 3143.

T. Mukherjee , S.V. Iyer , & G. Fedder (1998), Optimization-based synthesis of microresonators. Sensors and Actuators A: Physical 70(1–2), 118127.

S. Narayanan , & S. Azarm (1999). On improving multiobjective genetic algorithms for design optimization. Structural Optimization 18, 146155.

M. Peysakhov , & W.C. Regli (2003). Using assembly representations to enable evolutionary design of lego structures. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(2), 155168.

N. Srinivas , & K. Deb (1995). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221248.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords: