Implicitly Controlling Bloat in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Whigham:2010:ieeeTEC,
-
author = "Peter A. Whigham and Grant Dick",
-
title = "Implicitly Controlling Bloat in Genetic Programming",
-
journal = "IEEE Transactions on Evolutionary Computation",
-
year = "2010",
-
volume = "14",
-
number = "2",
-
pages = "173--190",
-
month = apr,
-
keywords = "genetic algorithms, genetic programming, Bloat, Book
reviews, Convergence, Data mining, Mathematical model,
Pediatrics, elitism, inbreeding, spatially-structured
evolutionary algorithm",
-
ISSN = "1089-778X",
-
DOI = "doi:10.1109/TEVC.2009.2027314",
-
size = "18 pages",
-
abstract = "During the evolution of solutions using genetic
programming (GP) there is generally an increase in
average tree size without a corresponding increase in
fitness---a phenomenon commonly referred to as bloat.
Although previously studied from theoretical and
practical viewpoints there has been little progress in
deriving controls for bloat which do not explicitly
refer to tree size. Here, the use of spatial population
structure in combination with local elitist replacement
is shown to reduce bloat without a subsequent loss of
performance. Theoretical concepts regarding inbreeding
and the role of elitism are used to support the
described approach. The proposed system behavior is
confirmed via extensive computer simulations on
benchmark problems. The main practical result is that
by placing a population on a torus, with selection
defined by a Moore neighborhood and local elitist
replacement, bloat can be substantially reduced without
compromising performance.",
-
notes = "also known as \cite{5352336}",
- }
Genetic Programming entries for
Peter Alexander Whigham
Grant Dick
Citations