Two Ways of Discovering the Size and Shape of a Computer Program to Solve a Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Koza:1995:2ss,
-
author = "John R. Koza",
-
title = "Two Ways of Discovering the Size and Shape of a
Computer Program to Solve a Problem",
-
booktitle = "Genetic Algorithms: Proceedings of the Sixth
International Conference (ICGA95)",
-
year = "1995",
-
editor = "Larry J. Eshelman",
-
pages = "287--294",
-
address = "Pittsburgh, PA, USA",
-
publisher_address = "San Francisco, CA, USA",
-
month = "15-19 " # jul,
-
publisher = "Morgan Kaufmann",
-
keywords = "genetic algorithms, genetic programming",
-
ISBN = "1-55860-370-0",
-
URL = "http://www.genetic-programming.com/jkpdf/icga1995.pdf",
-
abstract = "The requirement that the user predetermine the size
and shape of the ultimate solution to a problem has
been a bane of automated machine learning from the
earliest times. This paper compares two techniques for
automatically discovering, during a run of genetic
programming, the architecture of a multi-part computer
program while concurrently solving the problem. In the
first technique, called evolutionary selection, the
initial random population is architecturally diverse
and there is a competition during the run among the
various architectures while they are trying to solve
the problem. The second technique, called evolution of
architecture, employs six new architecture-altering
operations that provide a way to evolve the
architecture of a multi-part program in the sense of
actually changing the architecture of the program
dynamically during the run. The new
architecture-altering operations are motivated by the
naturally occurring operation of gene duplication, as
described in Susumu Ohno's provocative book Evolution
by Means of Gene Duplication.",
- }
Genetic Programming entries for
John Koza
Citations