Abstention Reduces Errors--decision Abstaining N-version Genetic Programming

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

  author =       "Kosuke Imamura and Robert B. Heckendorn and 
                 Terence Soule and James A. Foster",
  title =        "Abstention Reduces Errors--decision Abstaining
                 {N}-version Genetic Programming",
  booktitle =    "GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference",
  editor =       "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and 
                 R. Roy and D. Davis and R. Poli and K. Balakrishnan and 
                 V. Honavar and G. Rudolph and J. Wegener and 
                 L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and 
                 E. Burke and N. Jonoska",
  year =         "2002",
  pages =        "796--803",
  address =      "New York",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "9-13 " # jul,
  publisher =    "Morgan Kaufmann Publishers",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-878-8",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP169.ps",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP169.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
  URL =          "http://dl.acm.org/citation.cfm?id=646205.682481",
  acmid =        "682481",
  abstract =     "Optimal fault masking N-Version Genetic Programming
                 (NVGP) is a technique for building fault tolerant
                 software via ensemble of automatically generated
                 modules in such a way as to maximise their collective
                 fault masking ability. Decision Abstaining N-Version
                 Genetic Programming is NVGP that abstains from
                 decision-making, when there is no decisive vote among
                 the modules to make a decision. A special course of
                 action may be taken for an abstained instance. We found
                 that decision abstention contributed to error reduction
                 in our experimental Escherichia coli DNA promoter
                 sequence classification problem. Though decision
                 abstention may reduce errors, high abstention rate
                 makes the system of little use. This paper investigates
                 the trade-off between abstention rate and error
  notes =        "GECCO-2002. A joint meeting of the eleventh
                 International Conference on Genetic Algorithms
                 (ICGA-2002) and the seventh Annual Genetic Programming
                 Conference (GP-2002)",

Genetic Programming entries for Kosuke Imamura Robert B Heckendorn Terence Soule James A Foster