Automatically Defined Functions in Gene Expression
Programming
C. Ferreira. Genetic Systems Programming: Theory and Experiences, volume 13 of Studies in Computational Intelligence, Springer, Germany, (2006)
Abstract
In this chapter it is shown how Automatically Defined
Functions are encoded in the genotype/phenotype system
of Gene Expression Programming. As an introduction, the
fundamental differences between Gene Expression
Programming and its predecessors, Genetic Algorithms
and Genetic Programming, are briefly summarized so that
the evolutionary advantages of Gene Expression
Programming are better understood. The introduction
proceeds with a detailed description of the
architecture of the main players of Gene Expression
Programming (chromosomes and expression trees),
focusing mainly on the interactions between them and
how the simple, yet revolutionary, structure of the
chromosomes allows the efficient, unconstrained
exploration of the search space. The work proceeds with
an introduction to Automatically Defined Functions and
how they are implemented in Gene Expression
Programming. Furthermore, the importance of
Automatically Defined Functions in Evolutionary
Computation is thoroughly analyzed by comparing the
performance of sophisticated learning systems with
Automatically Defined Functions with much simpler ones
on the sextic polynomial problem.
%0 Book Section
%1 Ferreira:2006:GSP
%A Ferreira, Cândida
%B Genetic Systems Programming: Theory and Experiences
%C Germany
%D 2006
%E Nedjah, Nadia
%E Abraham, Ajith
%E de Macedo
Mourelle, Luiza
%I Springer
%K ADF algorithms, expression gene genetic programming,
%P 21--56
%T Automatically Defined Functions in Gene Expression
Programming
%U http://www.gene-expression-programming.com/webpapers/Ferreira-GSP2006.pdf
%V 13
%X In this chapter it is shown how Automatically Defined
Functions are encoded in the genotype/phenotype system
of Gene Expression Programming. As an introduction, the
fundamental differences between Gene Expression
Programming and its predecessors, Genetic Algorithms
and Genetic Programming, are briefly summarized so that
the evolutionary advantages of Gene Expression
Programming are better understood. The introduction
proceeds with a detailed description of the
architecture of the main players of Gene Expression
Programming (chromosomes and expression trees),
focusing mainly on the interactions between them and
how the simple, yet revolutionary, structure of the
chromosomes allows the efficient, unconstrained
exploration of the search space. The work proceeds with
an introduction to Automatically Defined Functions and
how they are implemented in Gene Expression
Programming. Furthermore, the importance of
Automatically Defined Functions in Evolutionary
Computation is thoroughly analyzed by comparing the
performance of sophisticated learning systems with
Automatically Defined Functions with much simpler ones
on the sextic polynomial problem.
%@ 3-540-29849-5
@incollection{Ferreira:2006:GSP,
abstract = {
In this chapter it is shown how Automatically Defined
Functions are encoded in the genotype/phenotype system
of Gene Expression Programming. As an introduction, the
fundamental differences between Gene Expression
Programming and its predecessors, Genetic Algorithms
and Genetic Programming, are briefly summarized so that
the evolutionary advantages of Gene Expression
Programming are better understood. The introduction
proceeds with a detailed description of the
architecture of the main players of Gene Expression
Programming (chromosomes and expression trees),
focusing mainly on the interactions between them and
how the simple, yet revolutionary, structure of the
chromosomes allows the efficient, unconstrained
exploration of the search space. The work proceeds with
an introduction to Automatically Defined Functions and
how they are implemented in Gene Expression
Programming. Furthermore, the importance of
Automatically Defined Functions in Evolutionary
Computation is thoroughly analyzed by comparing the
performance of sophisticated learning systems with
Automatically Defined Functions with much simpler ones
on the sextic polynomial problem.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Germany},
author = {Ferreira, C\^{a}ndida},
biburl = {https://www.bibsonomy.org/bibtex/2931cb856b445bac8dd800f0a410d432a/brazovayeye},
booktitle = {Genetic Systems Programming: Theory and Experiences},
editor = {Nedjah, Nadia and Abraham, Ajith and {de Macedo
Mourelle}, Luiza},
interhash = {fd47baf822019e3baa2b4bc003243df9},
intrahash = {931cb856b445bac8dd800f0a410d432a},
isbn = {3-540-29849-5},
keywords = {ADF algorithms, expression gene genetic programming,},
notes = {http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html},
pages = {21--56},
publisher = {Springer},
series = {Studies in Computational Intelligence},
timestamp = {2008-06-19T17:39:35.000+0200},
title = {Automatically Defined Functions in Gene Expression
Programming},
url = {http://www.gene-expression-programming.com/webpapers/Ferreira-GSP2006.pdf},
volume = 13,
year = 2006
}