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Evolutionary Algorithms in the Optimal Sizing of Analog Circuits

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Intelligent Computational Optimization in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 366))

Abstract

Analog signal processing applications such as filter design and oscillators, require the use of different kinds of amplifiers, namely: voltage follower, current conveyors, operational amplifiers and current feedback operational amplifiers. To improve the performances on these applications, it is very much needed to optimize the behavior of the amplifiers. That way, this work shows their optimization by applying two evolutionary algorithms: the Non-Sorting Genetic Algorithm (NSGA-II), and the Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). The analog circuits are sized taking into account design constraints, and linking HSPICE like circuit simulator to evaluate their electrical characteristics. Additionally, we show that differential evolution (DE) enhances the convergence to the Pareto front and controls the evolution of the objectives among different runs. DE also preserves the same time efficiency and increases the dominance on NSGA-II and MOEA/D compared with the one point crossover genetic operator.

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Author information

Authors and Affiliations

  1. INAOE, Mexico

    Esteban Tlelo-Cuautle, Ivick Guerra-Gómez, Carlos Alberto Reyes-García & Gustavo Rodríguez-Gómez

  2. Computer Science Department, CINVESTAV, Mexico

    Luis Gerardo de la Fraga

  3. Instituto Tecnológico de Puebla, Mexico

    Georgina Flores-Becerra & Said Polanco-Martagón

  4. University of Sfax, Tunisia

    Mourad Fakhfakh

  5. CENIDET, Mexico

    Gerardo Reyes-Salgado

Authors
  1. Esteban Tlelo-Cuautle
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  2. Ivick Guerra-Gómez
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  3. Luis Gerardo de la Fraga
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  4. Georgina Flores-Becerra
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  5. Said Polanco-Martagón
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  6. Mourad Fakhfakh
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  7. Carlos Alberto Reyes-García
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  8. Gustavo Rodríguez-Gómez
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  9. Gerardo Reyes-Salgado
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Editor information

Editors and Affiliations

  1. Dept. Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, 820-8502, Izuka-Shi, Fukuoka, Japan

    Mario Köppen

  2. School of Engineering and Applied Science, Aston University, Aston Triangle, B4 7ET, Birmingham, U.K.

    Gerald Schaefer

  3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

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Tlelo-Cuautle, E. et al. (2011). Evolutionary Algorithms in the Optimal Sizing of Analog Circuits. In: Köppen, M., Schaefer, G., Abraham, A. (eds) Intelligent Computational Optimization in Engineering. Studies in Computational Intelligence, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_5

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  • DOI: https://doi.org/10.1007/978-3-642-21705-0_5

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  • Online ISBN: 978-3-642-21705-0

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