Automated discovery of detectors and iteration-performing calculations to recognize patterns in protein sequences using genetic Programming

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

  author =       "John R. Koza",
  title =        "Automated discovery of detectors and
                 {iteration-performing} calculations to recognize
                 patterns in protein sequences using genetic
  booktitle =    "Proceedings of the Conference on Computer Vision and
                 Pattern Recognition",
  year =         "1994",
  pages =        "684--689",
  publisher =    "IEEE Computer Society Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  size =         "7 pages",
  abstract =     "This paper describes an automated process for the
                 dynamic creation of a pattern-recognizing computer
                 program consisting of initially-unknown detectors, an
                 initially-unknown iterative calculation incorporating
                 the as-yet-uncreated detectors, and an
                 initially-unspecified final calculation incorporating
                 the results of the as-yet-uncreated iteration. The
                 program's goal is to recognize a given protein segment
                 as being a transmembrane domain or non-transmembrane
                 area. The recognizing program to solve this problem
                 will be evolved using the recently-developed genetic
                 programming paradigm. Genetic programming starts with a
                 primordial ooze of randomly generated computer programs
                 composed of available programmatic ingredients and then
                 genetically breeds the population using the Darwinian
                 principle of survival of the fittest and the genetic
                 crossover (sexual recombination) operation. Automatic
                 function definition enables genetic programming to
                 dynamically create subroutines (detectors). When
                 cross-validated, the best genetically-evolved
                 recognizer achieves an out-of-sample correlation of
                 0.968 and an out-of-sample error rate of 1.6%. This
                 error rate is better than that recently reported for
                 five other methods.

  notes =        "


Genetic Programming entries for John Koza