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Passive Analog Filter Design Using GP Population Control Strategies

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Book cover Opportunities and Challenges for Next-Generation Applied Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 214))

Abstract

This paper presents the use of two different strategies for genetic programming (GP) population growth control: decreasing the computational effort by plagues and dynamic adjustment of fitness; applied to passive analog filters design based on general topologies. Obtained experimental results show that proposed strategies improve the design process performance.

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Author information

Authors and Affiliations

  1. School of Engineering, Intelligent Systems Laboratory, University of Buenos Aires, Paseo Colon 850, (C1063ACV), Buenos Aires, Argentina

    Mariano Chouza & Ramón García-Martínez

  2. Master Program on Software Engineering, Buenos Aires Institute of Technology, Av. Madero 399, (C1063ACV), Buenos Aires, Argentina

    Claudio Rancan

  3. Computer Science School, PhD Program, University of La Plata, Calles 50 y 120, (B1900), La Plata, Buenos Aires, Argentina

    Claudio Rancan & Ramón García-Martínez

  4. School of Engineering, Distributed Systems Laboratory, University of Buenos Aires, Paseo Colon 850, (C1063ACV), Buenos Aires, Argentina

    Osvaldo Clua

Authors
  1. Mariano Chouza
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  2. Claudio Rancan
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  3. Osvaldo Clua
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  4. Ramón García-Martínez
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information on Engineering, National University of Tainan, 33, Sec. 2, Su-Lin St., 700, Tainan City, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Nanzih District, 700, Kaohsiung University Rd., 811, Kaohsiung, Taiwan

    Tzung-Pei Hong

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© 2009 Springer-Verlag Berlin Heidelberg

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Chouza, M., Rancan, C., Clua, O., García-Martínez, R. (2009). Passive Analog Filter Design Using GP Population Control Strategies. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_24

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  • DOI: https://doi.org/10.1007/978-3-540-92814-0_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92813-3

  • Online ISBN: 978-3-540-92814-0

  • eBook Packages: EngineeringEngineering (R0)

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