Representation and Structural Biases in CGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Payne:2009:cec,
-
author = "Andrew J. Payne and Susan Stepney",
-
title = "Representation and Structural Biases in CGP",
-
booktitle = "2009 IEEE Congress on Evolutionary Computation",
-
year = "2009",
-
editor = "Andy Tyrrell",
-
pages = "1064--1071",
-
address = "Trondheim, Norway",
-
month = "18-21 " # may,
-
organization = "IEEE Computational Intelligence Society",
-
publisher = "IEEE Press",
-
isbn13 = "978-1-4244-2959-2",
-
file = "P019.pdf",
-
DOI = "doi:10.1109/CEC.2009.4983064",
-
abstract = "An evolutionary algorithm automatically discovers
suitable solutions to a problem, which may lie anywhere
in a large search space of candidate solutions. In the
case of Genetic Programming, this means performing an
efficient search of all possible computer programs
represented as trees. Exploration of the search space
appears to be constrained by structural mechanisms that
exist in Genetic Programming as a consequence of using
trees to represent solutions. As a result, programs
with certain structures are more likely to be evolved,
and others extremely unlikely. We investigate whether
the graph representation used in Cartesian Genetic
Programming causes an analogous biasing effect,
imposing natural limitations on the class of solution
structures that are likely to be evolved.
Representation bias and structural bias are identified:
the rarer {"}regular{"} structures appear to be easier
to evolve than more common {"}irregular{"} ones.",
-
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
-
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
- }
Genetic Programming entries for
Andrew J Payne
Susan Stepney
Citations