Genetic Programming Operators Applied to Genetic
Algorithms
D. Vrajitoru. Proceedings of the Genetic and Evolutionary
Computation Conference, 1, page 686--693. Orlando, Florida, USA, Morgan Kaufmann, (13-17 July 1999)
Abstract
Like other learning paradigms, the performance of the
genetic algorithms (GAs) is dependent on the parameter
choice, on the problem representation, and on the
fitness landscape. Accordingly, a GA can show good or
weak results even when applied on the same problem.
Following this idea, the crossover operator plays an
important role, and its study is the object of the
present paper. A mathematical analysis has led us to
construct a new form of crossover operator inspired
from genetic programming (GP) that we have already
applied in field of information retrieval. In this
paper we extend the previous results and compare the
new operator with several known crossover operators
under various experimental conditions
Proceedings of the Genetic and Evolutionary
Computation Conference
year
1999
month
13-17 July
pages
686--693
publisher
Morgan Kaufmann
volume
1
publisher_address
San Francisco, CA 94104, USA
isbn
1-55860-611-4
notes
GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)
%0 Conference Paper
%1 vrajitoru:1999:GPOAGA
%A Vrajitoru, Dana
%B Proceedings of the Genetic and Evolutionary
Computation Conference
%C Orlando, Florida, USA
%D 1999
%E Banzhaf, Wolfgang
%E Daida, Jason
%E Eiben, Agoston E.
%E Garzon, Max H.
%E Honavar, Vasant
%E Jakiela, Mark
%E Smith, Robert E.
%I Morgan Kaufmann
%K algorithms, classifier genetic programming, systems
%P 686--693
%T Genetic Programming Operators Applied to Genetic
Algorithms
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-312.ps.gz
%V 1
%X Like other learning paradigms, the performance of the
genetic algorithms (GAs) is dependent on the parameter
choice, on the problem representation, and on the
fitness landscape. Accordingly, a GA can show good or
weak results even when applied on the same problem.
Following this idea, the crossover operator plays an
important role, and its study is the object of the
present paper. A mathematical analysis has led us to
construct a new form of crossover operator inspired
from genetic programming (GP) that we have already
applied in field of information retrieval. In this
paper we extend the previous results and compare the
new operator with several known crossover operators
under various experimental conditions
%@ 1-55860-611-4
@inproceedings{vrajitoru:1999:GPOAGA,
abstract = {Like other learning paradigms, the performance of the
genetic algorithms (GAs) is dependent on the parameter
choice, on the problem representation, and on the
fitness landscape. Accordingly, a GA can show good or
weak results even when applied on the same problem.
Following this idea, the crossover operator plays an
important role, and its study is the object of the
present paper. A mathematical analysis has led us to
construct a new form of crossover operator inspired
from genetic programming (GP) that we have already
applied in field of information retrieval. In this
paper we extend the previous results and compare the
new operator with several known crossover operators
under various experimental conditions},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Orlando, Florida, USA},
author = {Vrajitoru, Dana},
biburl = {https://www.bibsonomy.org/bibtex/249c8bb3530f23fe895237fc589734c47/brazovayeye},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference},
editor = {Banzhaf, Wolfgang and Daida, Jason and Eiben, Agoston E. and Garzon, Max H. and Honavar, Vasant and Jakiela, Mark and Smith, Robert E.},
interhash = {008e610269ee04d49de3354d94eb03d0},
intrahash = {49c8bb3530f23fe895237fc589734c47},
isbn = {1-55860-611-4},
keywords = {algorithms, classifier genetic programming, systems},
month = {13-17 July},
notes = {GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)},
pages = {686--693},
publisher = {Morgan Kaufmann},
publisher_address = {San Francisco, CA 94104, USA},
timestamp = {2008-06-19T17:53:42.000+0200},
title = {Genetic Programming Operators Applied to Genetic
Algorithms},
url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-312.ps.gz},
volume = 1,
year = 1999
}