A New Crossover Operator in GP for Object
Classification
M. Zhang, X. Gao, and W. Lou. CS-TR-06-2. Computer Science, Victoria University of Wellington, New Zealand, (January 2006)
Abstract
instead of randomly choosing the crossover points as
in the standard crossover operator, we use a measure
called looseness to guide the selection of crossover
points. Rather than using the genetic beam search only,
this approach uses a hybrid beam-hill climbing search
scheme in the evolutionary process. This approach is
examined and compared with the standard crossover
operator and the headless chicken crossover method on a
sequence of object classification problems. The results
suggest that this approach outperforms both the
headless chicken crossover and the standard crossover
on all of these problems.
%0 Report
%1 vuw-CS-TR-06-2
%A Zhang, Mengjie
%A Gao, Xiaoying
%A Lou, Weijun
%C New Zealand
%D 2006
%K Crossover algorithms, controlled crossover, genetic hybrid looseness points, programming, search
%N CS-TR-06-2
%T A New Crossover Operator in GP for Object
Classification
%U http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-2.pdf
%X instead of randomly choosing the crossover points as
in the standard crossover operator, we use a measure
called looseness to guide the selection of crossover
points. Rather than using the genetic beam search only,
this approach uses a hybrid beam-hill climbing search
scheme in the evolutionary process. This approach is
examined and compared with the standard crossover
operator and the headless chicken crossover method on a
sequence of object classification problems. The results
suggest that this approach outperforms both the
headless chicken crossover and the standard crossover
on all of these problems.
@techreport{vuw-CS-TR-06-2,
abstract = {instead of randomly choosing the crossover points as
in the standard crossover operator, we use a measure
called looseness to guide the selection of crossover
points. Rather than using the genetic beam search only,
this approach uses a hybrid beam-hill climbing search
scheme in the evolutionary process. This approach is
examined and compared with the standard crossover
operator and the headless chicken crossover method on a
sequence of object classification problems. The results
suggest that this approach outperforms both the
headless chicken crossover and the standard crossover
on all of these problems.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {New Zealand},
author = {Zhang, Mengjie and Gao, Xiaoying and Lou, Weijun},
biburl = {https://www.bibsonomy.org/bibtex/27b0dfaebebe8a848eae8598e55f1ce81/brazovayeye},
institution = {Computer Science, Victoria University of Wellington},
interhash = {4b4f7178c2cdb35323ce9e5c44bd507e},
intrahash = {7b0dfaebebe8a848eae8598e55f1ce81},
keywords = {Crossover algorithms, controlled crossover, genetic hybrid looseness points, programming, search},
month = {January},
number = {CS-TR-06-2},
size = {17 pages},
timestamp = {2008-06-19T17:55:36.000+0200},
title = {A New Crossover Operator in {GP} for Object
Classification},
url = {http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-2.pdf},
year = 2006
}