Classifying protein segments as transmembrane domains using genetic programming and architecture-altering operations

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

@InCollection{koza:1997:cpstdGP,
  author =       "John R. Koza",
  title =        "Classifying protein segments as transmembrane domains
                 using genetic programming and architecture-altering
                 operations",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and David B. Fogel and 
                 Zbigniew Michalewicz",
  chapter =      "section G6.1",
  pages =        "G6.1:1--5",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  URL =          "http://www.genetic-programming.com/jkpdf/hectransmembrane1997.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
  DOI =          "doi:10.1201/9781420050387.ptg",
  size =         "5 pages",
  abstract =     "The goal of automatic programming is to create, in an
                 automated way, a computer program that enables a
                 computer to solve a problem. Ideally, an automatic
                 programming system should require that the user
                 pre-specify as little as possible about the problem. In
                 particular, it is desirable that the user not be
                 required to specify the size and shape (i.e., the
                 architecture) of the ultimate solution to the problem
                 before applying the technique. This paper describes how
                 the biological theory of gene duplication described in
                 Susumu Ohno's provocative book, Evolution by Means of
                 Gene Duplication, was brought to bear on a vexatious
                 problem from the domain of automated machine learning
                 in the computer science field. The resulting
                 biologically-motivated approach using six new
                 architecture-altering operations enables genetic
                 programming to automatically discover the size and
                 shape of the solution at the same time as it is
                 evolving a solution to the problem

                 Genetic programming with the architecture-altering
                 operations was used to evolve a computer program to
                 classify a given protein segment as being a
                 transmembrane domain or non-transmembrane area of the
                 protein (without biochemical knowledge, such as
                 hydrophobicity values). The best genetically-evolved
                 program achieved an out-of-sample error rate that was
                 better than that reported for other previously reported
                 human-constructed algorithms. This is an instance of an
                 automated machine learning algorithm that is
                 competitive with human performance on a non-trivial
                 problem.",
  notes =        "memory cell M0",
}

Genetic Programming entries for John Koza

Citations