Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@Misc{nordin:2000:patent,
author = "Peter Nordin and Wolfgang Banzhaf",
title = "Computer implemented machine learning method and
system",
howpublished = "U.S. Patent 6128607",
year = "2000",
month = "3 " # oct,
keywords = "genetic algorithms, genetic programming",
abstract = "One or more machine code entities such as functions
are created which represent solutions to a problem and
are directly executable by a computer. The programs are
created and altered by a program in a higher level
language such as {"}C{"} which is not directly
executable, but requires translation into executable
machine code through compilation, interpretation,
translation, etc. The entities are initially created as
an integer array that can be altered by the program as
data, and are executed by the program by recasting a
pointer to the array as a function type. The entities
are evaluated by executing them with training data as
inputs, and calculating fitnesses based on a
predetermined criterion. The entities are then altered
based on their fitnesses using a machine learning
algorithm by recasting the pointer to the array as a
data (e.g. integer) type. This process is iteratively
repeated until an end criterion is reached. The
entities evolve in such a manner as to improve their
fitness, and one entity is ultimately produced which
represents an optimal solution to the problem. Each
entity includes a plurality of directly executable
machine code instructions, a header, a footer, and a
return instruction. The instructions include branch
instructions which enable subroutines, leaf functions,
external function calls, recursion, and loops. The
system can be implemented on an integrated circuit
chip, with the entities stored in high speed memory in
a central processing unit.",
notes = "6,128,607",
}
Genetic Programming entries for Peter Nordin Wolfgang Banzhaf