Two-Step Genetic Programming for Optimization of RNA Common-Structure

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{nam:evows04,
  author =       "Jin-Wu Nam and Je-Gun Joung and Young-Sirk Ahn and 
                 Byoung-Tak Zhang",
  title =        "Two-Step Genetic Programming for Optimization of {RNA}
                 Common-Structure",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoMUSART}, {EvoSTOC}",
  year =         "2004",
  month =        "5-7 " # apr,
  editor =       "Guenther R. Raidl and Stefano Cagnoni and 
                 Jurgen Branke and David W. Corne and Rolf Drechsler and 
                 Yaochu Jin and Colin R. Johnson and Penousal Machado and 
                 Elena Marchiori and Franz Rothlauf and George D. Smith and 
                 Giovanni Squillero",
  series =       "LNCS",
  volume =       "3005",
  address =      "Coimbra, Portugal",
  publisher =    "Springer Verlag",
  publisher_address = "Berlin",
  pages =        "73--83",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation",
  ISBN =         "3-540-21378-3",
  URL =          "http://bi.snu.ac.kr/Publications/Journals/International/LNCS_3005_73-83(2004).pdf",
  DOI =          "doi:10.1007/978-3-540-24653-4_8",
  abstract =     "We present an algorithm for identifying putative
                 ncRNAs using an RCSG (RNA Common Structural Grammar)
                 and show the effectiveness of the algorithm. The
                 algorithm consists of two steps: structure learning
                 step and sequence learning step. Both steps are based
                 on genetic programming. Generally genetic programming
                 has been applied to learn programs automatically,
                 reconstruct networks, and predict protein secondary
                 structures. In this study, we have applied genetic
                 programming to optimise structural grammars. The
                 structural grammars can be formulated by some rules as
                 tree structure including function variables. They can
                 be learned by genetic programming. We have defined the
                 rules on how structure definition grammars can be
                 encoded into function trees. The results we obtained
                 from the experiments with RCSG of tRNA and 5S small
                 rRNA and demonstrate the efficiency of our algorithm.",
  notes =        "EvoWorkshops2004

                 esRCSG used in \cite{Nam:2005:NAR}",
}

Genetic Programming entries for Jin-Wu Nam Je-Gun Joung Young-Sirk Ahn Byoung-Tak Zhang

Citations