We introduce a genetic algorithm (GA) with a new
representation method which we call the proportional GA
(PGA). The PGA is a multi-character GA that relies on
the existence or non-existence of genes to determine
the information that is expressed. The information
represented by a PGA individual depends only on what is
present on the individual and not on the order in which
it is present. As a result, the order of the encoded
information is free to evolve in response factors other
than the value of the solution, for example, in
response to the identification and formation of
building blocks. The PGA is also able to dynamically
evolve the resolution of encoded information. In this
paper, we describe our motivations for developing this
representation and provide a detailed description of a
PGA along with discussion of its benefits and
drawbacks. We compare the behavior of a PGA with that
of a canonical GA (CGA) and discuss conclusions and
future work based on these preliminary studies.
%0 Journal Article
%1 AnnieSWu:2002:GPEM
%A Wu, Annie S.
%A Garibay, Ivan
%D 2002
%J Genetic Programming and Evolvable Machines
%K algorithm algorithms, expression, gene genetic proportional representation,
%N 2
%P 157--192
%R doi:10.1023/A:1015531909333
%T The Proportional Genetic Algorithm: Gene Expression in
a Genetic Algorithm
%V 3
%X We introduce a genetic algorithm (GA) with a new
representation method which we call the proportional GA
(PGA). The PGA is a multi-character GA that relies on
the existence or non-existence of genes to determine
the information that is expressed. The information
represented by a PGA individual depends only on what is
present on the individual and not on the order in which
it is present. As a result, the order of the encoded
information is free to evolve in response factors other
than the value of the solution, for example, in
response to the identification and formation of
building blocks. The PGA is also able to dynamically
evolve the resolution of encoded information. In this
paper, we describe our motivations for developing this
representation and provide a detailed description of a
PGA along with discussion of its benefits and
drawbacks. We compare the behavior of a PGA with that
of a canonical GA (CGA) and discuss conclusions and
future work based on these preliminary studies.
@article{AnnieSWu:2002:GPEM,
abstract = {We introduce a genetic algorithm (GA) with a new
representation method which we call the proportional GA
(PGA). The PGA is a multi-character GA that relies on
the existence or non-existence of genes to determine
the information that is expressed. The information
represented by a PGA individual depends only on what is
present on the individual and not on the order in which
it is present. As a result, the order of the encoded
information is free to evolve in response factors other
than the value of the solution, for example, in
response to the identification and formation of
building blocks. The PGA is also able to dynamically
evolve the resolution of encoded information. In this
paper, we describe our motivations for developing this
representation and provide a detailed description of a
PGA along with discussion of its benefits and
drawbacks. We compare the behavior of a PGA with that
of a canonical GA (CGA) and discuss conclusions and
future work based on these preliminary studies.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Wu, Annie S. and Garibay, Ivan},
biburl = {https://www.bibsonomy.org/bibtex/2b80983e243285fbd4f4e4983f8715877/brazovayeye},
doi = {doi:10.1023/A:1015531909333},
interhash = {1a4d604818d68f47f933d7e1085c448f},
intrahash = {b80983e243285fbd4f4e4983f8715877},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {algorithm algorithms, expression, gene genetic proportional representation,},
month = {June},
notes = {Special issue on Gene Expression
\cite{Kargupta:2002:GPEM} Article ID: 408587},
number = 2,
pages = {157--192},
timestamp = {2008-06-19T17:54:33.000+0200},
title = {The Proportional Genetic Algorithm: Gene Expression in
a Genetic Algorithm},
volume = 3,
year = 2002
}