Evolutionary Robust Design of Analog Filters Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jianjun Hu and Shaobo Li and Erik D. Goodman",
  title =        "Evolutionary Robust Design of Analog Filters Using
                 Genetic Programming",
  booktitle =    "Evolutionary Computation in Dynamic and Uncertain
  publisher =    "Springer",
  year =         "2007",
  editor =       "Shengxiang Yang and Yew-Soon Ong and Yaochu Jin",
  volume =       "51",
  series =       "Studies in Computational Intelligence",
  pages =        "479--496",
  chapter =      "21",
  email =        "hujianju@gmail.com",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-49772-1",
  URL =          "http://www.springerlink.com/content/1w71041124712n78/",
  DOI =          "doi:10.1007/978-3-540-49774-5_21",
  abstract =     "This chapter proposes a robust design approach that
                 exploits the open ended topological synthesis
                 capability of genetic programming (GP) to evolve robust
                 low pass and high pass analog filters. Compared with a
                 traditional robust design approach based on genetic
                 algorithms (GAs), the open-ended topology search based
                 on genetic programming and bond graph modeling (GPBG)
                 is shown to be able to evolve more robust filters with
                 respect to parameter perturbations than what was
                 achieved through parameter tuning alone, for the test
  notes =        "http://www.cse.sc.edu/~jianjunh/",

Genetic Programming entries for Jianjun Hu Shaobo Li Erik Goodman