- 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",
- broken = "doi:10.1201/9781420050387.ptg",
-
URL = "
https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921",
- 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",
