A comparison between four Genetic Programming
techniques is presented in this paper. The compared
methods are Multi-Expression Programming, Gene
Expression Programming, Grammatical Evolution, and
Linear Genetic Programming. The comparison includes all
aspects of the considered evolutionary algorithms:
individual representation, fitness assignment, genetic
operators, and evolutionary scheme. Several numerical
experiments using five benchmarking problems are
carried out. Two test problems are taken from PROBEN1
and contain real-world data. The results reveal that
Multi-Expression Programming has the best overall
behavior for the considered test problems, closely
followed by Linear Genetic Programming.
%0 Journal Article
%1 oltean:2004:CS
%A Oltean, Mihai
%A Grosan, Crina
%D 2004
%J Complex Systems
%K GEP, MEP algorithms, evolution, genetic grammatical programming,
%N 4
%T A Comparison of Several Linear Genetic Programming
Techniques
%U http://www.cs.ubbcluj.ro/~cgrosan/030409_edited.pdf
%V 14
%X A comparison between four Genetic Programming
techniques is presented in this paper. The compared
methods are Multi-Expression Programming, Gene
Expression Programming, Grammatical Evolution, and
Linear Genetic Programming. The comparison includes all
aspects of the considered evolutionary algorithms:
individual representation, fitness assignment, genetic
operators, and evolutionary scheme. Several numerical
experiments using five benchmarking problems are
carried out. Two test problems are taken from PROBEN1
and contain real-world data. The results reveal that
Multi-Expression Programming has the best overall
behavior for the considered test problems, closely
followed by Linear Genetic Programming.
@article{oltean:2004:CS,
abstract = {A comparison between four Genetic Programming
techniques is presented in this paper. The compared
methods are Multi-Expression Programming, Gene
Expression Programming, Grammatical Evolution, and
Linear Genetic Programming. The comparison includes all
aspects of the considered evolutionary algorithms:
individual representation, fitness assignment, genetic
operators, and evolutionary scheme. Several numerical
experiments using five benchmarking problems are
carried out. Two test problems are taken from PROBEN1
and contain real-world data. The results reveal that
Multi-Expression Programming has the best overall
behavior for the considered test problems, closely
followed by Linear Genetic Programming.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Oltean, Mihai and Grosan, Crina},
biburl = {https://www.bibsonomy.org/bibtex/2bb84ebba1c37b360284ff7d9fd75e198/brazovayeye},
interhash = {243ee58c1e6c11352a578841b3284e61},
intrahash = {bb84ebba1c37b360284ff7d9fd75e198},
issn = {0891-2513},
journal = {Complex Systems},
keywords = {GEP, MEP algorithms, evolution, genetic grammatical programming,},
number = 4,
size = {29 pages},
timestamp = {2008-06-19T17:48:51.000+0200},
title = {A Comparison of Several Linear Genetic Programming
Techniques},
url = {http://www.cs.ubbcluj.ro/~cgrosan/030409_edited.pdf},
volume = 14,
year = 2004
}