Evolution of a computer program for classifying protein segments as transmembrane domains using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{koza:1994:cpstd,
  author =       "John R. Koza",
  title =        "Evolution of a computer program for classifying
                 protein segments as transmembrane domains using genetic
                 programming",
  booktitle =    "Proceedings of the Second International Conference on
                 Intelligent Systems for Molecular Biology",
  year =         "1994",
  editor =       "Russ Altman and Douglas Brutlag and Peter Karp and 
                 Richard Lathrop and David Searls",
  pages =        "244--252",
  publisher_address = "Menlo Park, CA, USA",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.com/jkpdf/ismb1994.pdf",
  abstract =     "The recently-developed genetic programming paradigm is
                 used to evolve a computer program to classify a given
                 protein segment as being a transmembrane domain or
                 non-transmembrane area of the protein. Genetic
                 programming starts with a primordial ooze of randomly
                 generated computer programs composed of available
                 programmatic ingredients and then genetically breeds
                 the population of programs using the Darwinian
                 principle of survival of the fittest and an analog of
                 the naturally occurring genetic operation of crossover
                 (sexual recombination). Automatic function definition
                 enables genetic programming to dynamically create
                 subroutines dynamically during the run. Genetic
                 programming is given a training set of
                 differently-sized protein segments and their correct
                 classification (but no biochemical knowledge, such as
                 hydrophobicity values). Correlation is used as the
                 fitness measure to drive the evolutionary process. The
                 best genetically-evolved program achieves an
                 out-of-sample correlation of 0.968 and an out-of-sample
                 error rate of 1.6percent. This error rate is better
                 than that reported for four other algorithms reported
                 at the First International Conference on Intelligent
                 Systems for Molecular Biology. Our genetically evolved
                 program is an instance of an algorithm discovered by an
                 automated learning paradigm that is superior to that
                 written by human investigators.",
}

Genetic Programming entries for John Koza

Citations