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

by

Koza, J., R.

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Info: Handbook of Evolutionary Computation, 1997, p. G6.1:1-5
Keywords:genetic algorithms, genetic programming
Abstract:
The goal of automatic programming [AP] is to create, in an automated way, a computer program that enables a computer to solve a problem. Ideally, an automatic programming system [AP] 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 [ML] in the computer science field. The resulting biologically-motivated approach using six new architecture-altering operations enables genetic programming to automatically [GP] discover the size and shape of the solution at the same time as it is evolving a solution to the problem Genetic programming [GP] 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 [ML] that is competitive with human performance on a non-trivial problem.
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BibTex:
@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 =       "T. Baeck and D. B. Fogel and Z. Michalewicz",
  pages =        "G6.1:1--5",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/~koza/HECtm.ps",
  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.",
}