Genetic Programming and Redundancy
Created by W.Langdon from
gpbibliography.bib Revision:1.7810
 @InProceedings{BT94,

author = "Tobias Blickle and Lothar Thiele",

title = "Genetic Programming and Redundancy",

booktitle = "Genetic Algorithms within the Framework of
Evolutionary Computation (Workshop at KI94,
Saarbr{\"u}cken)",

editor = "J. Hopf",

publisher = "MaxPlanckInstitut f{\"u}r Informatik
(MPII94241)",

address = "
Im Stadtwald, Building 44, D66123 Saarbr{\"u}cken,
Germany
",

pages = "3338",

year = "1994",

keywords = "genetic algorithms, genetic programming",

URL = "http://www.tik.ee.ethz.ch/~tec/publications/bt94/GPandRedundancy.ps.gz",

size = "6 pages",

abstract = "The Genetic Programming optimization method (GP)
elaborated by John Koza [Koza, 1992] is a variant of
Genetic Algorithms. The search space of the problem
domain consists of computer programs represented as
parse trees, and the crossover operator is realized by
an exchange of subtrees. Empirical analyses show that
large parts of those trees are never used or evaluated
which means that these parts of the trees are
irrelevant for the solution or redundant. This paper is
concerned with the identification of the redundancy
occurring in GP. It starts with a mathematical
description of the behaviour of GP and the conclusions
drawn from that description among others explain the
size problem which denotes the phenomenon that the
average size of trees in the population grows with
time.",

notes = "From GP list Wed, 22 Mar 95 we did some work on the
convergence problem and the redundancy in the trees in
GP. It turned out that bloating is a property of GP
that arises from the fact that more redundant trees
have a higher probability to survive crossover. As a
result, the redundant part of the trees grow bigger and
bigger because the increased proportion of redundant
cutsites in the tree again lead to a higher
probability to survive crossover.
Gives a formula for tournament size related to
proportion of crossover in a generational GP. Ie
recommending T=10 for pc=0.9. This does not apply to
steady state GA.
",
 }
Genetic Programming entries for
Tobias Blickle
Lothar Thiele
Citations