Automated passive filter synthesis using a novel tree representation and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "Automated passive filter synthesis using a novel tree
                 representation and genetic programming",
  author =       "Shoou-Jinn Chang and Hao-Sheng Hou and Yan-Kuin Su",
  journal =      "IEEE Transactions on Evolutionary Computation",
  volume =       "10",
  number =       "1",
  month =        feb,
  year =         "2006",
  pages =        "93--100",
  keywords =     "genetic algorithms, genetic programming, RLC circuits,
                 circuit optimisation, network topology, passive
                 filters, GP-evolved circuits, RLC circuit analysis,
                 automated passive filter synthesis, circuit topology,
                 tree representation, Circuit analysis, circuit
                 representation, passive filter synthesis",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2005.861415",
  abstract =     "This paper proposes a novel tree representation which
                 is suitable for the analysis of RLC (i.e., resistor,
                 inductor, and capacitor) circuits. Genetic programming
                 (GP) based on the tree representation is applied to
                 passive filter synthesis problems. The GP is optimised
                 and then incorporated into an algorithm which can
                 automatically find parsimonious solutions without
                 predetermining the number of the required circuit
                 components. The experimental results show the proposed
                 method is efficient in three aspects. First, the
                 GP-evolved circuits are more parsimonious than those
                 resulting from traditional design methods in many
                 cases. Second, the proposed method is faster than
                 previous work and can effectively generate parsimonious
                 filters of very high order where conventional methods
                 fail. Third, when the component values are restricted
                 to a set of preferred values, the GP method can
                 generate compliant solutions by means of novel circuit
  notes =        "INSPEC Accession Number:8753451

                 Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan,

Genetic Programming entries for Shoou-Jinn Chang Hao-Sheng Hou Yan-Kuin Su