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Electromagnetic Design Automation: Surrogate Model Assisted Evolutionary Algorithm

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Automated Design of Analog and High-frequency Circuits

Part of the book series: Studies in Computational Intelligence ((SCI,volume 501))

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Abstract

Chapter 7 reviews surrogate model assisted evolutionary algorithms and the application area: design automation of mm-wave integrated circuits and complex antennas. Two machine learning methods, Gaussian process and artificial neural networks are introduced.

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Notes

  1. 1.

    There are also simple GP modeling and blind GP modeling [49].

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Author information

Authors and Affiliations

  1. Department of Computing, Glyndwr University, Mold Road, Wrexham, Wales, LL11 2AW, UK

    Bo Liu

  2. Department of Elektrotechniek ESAT-MICAS, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B3001, Leuven, Belgium

    Georges Gielen

  3. IMSE-CNM, Universidad de Sevilla and CSIC, Avda. Americo Vespucio s/n, esq. Leonardo da Vinci Isla de l, E–41092, Sevilla, Spain

    Francisco V. Fernández

Authors
  1. Bo Liu
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  2. Georges Gielen
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  3. Francisco V. Fernández
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Correspondence to Bo Liu .

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Liu, B., Gielen, G., Fernández, F.V. (2014). Electromagnetic Design Automation: Surrogate Model Assisted Evolutionary Algorithm. In: Automated Design of Analog and High-frequency Circuits. Studies in Computational Intelligence, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39162-0_7

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  • DOI: https://doi.org/10.1007/978-3-642-39162-0_7

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  • Print ISBN: 978-3-642-39161-3

  • Online ISBN: 978-3-642-39162-0

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