An Analysis of the MAX Problem in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{langdon:1997:MAX,
-
author = "W. B. Langdon and R. Poli",
-
title = "An Analysis of the {MAX} Problem in Genetic
Programming",
-
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
-
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and
Rick L. Riolo",
-
year = "1997",
-
month = "13-16 " # jul,
-
pages = "222--230",
-
address = "Stanford University, CA, USA",
-
publisher_address = "San Francisco, CA, USA",
-
publisher = "Morgan Kaufmann",
-
keywords = "genetic algorithms, genetic programming",
-
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.max_gp97.pdf",
-
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.max_gp97.ps",
-
size = "9 pages",
-
abstract = "We present a detailed analysis of the evolution of GP
populations using the problem of finding a program
which returns the maximum possible value for a given
terminal and function set and a depth limit on the
program tree (known as the MAX problem). We confirm the
basic message of \cite{Gathercole:1996:aicrtd} that
crossover together with program size restrictions can
be responsible for premature convergence to a
sub-optimal solution. We show that this can happen even
when the population retains a high level of variety and
show that in many cases evolution from the sub-optimal
solution to the solution is possible if sufficient time
is allowed. In both cases theoretical models are
presented and compared with actual runs. Price's
Covariance and Selection Theorem is experimentally
tested on GP populations. It is shown to hold only in
some cases, in others program size restrictions cause
important deviation from its predictions.",
-
notes = "GP-97 Considerable update of \cite{Langdon97}",
- }
Genetic Programming entries for
William B Langdon
Riccardo Poli
Citations