- author = "John R. Koza",
- title = "Automated discovery of detectors and {iteration-performing} calculations to recognize patterns in protein sequences using genetic Programming",
- 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 = "
http://www.genetic-programming.com/jkpdf/cvpr1994.pdf",
- 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 = "
",
