The majority of current genetic algorithms (GAs),
while inspired by natural evolutionary systems, are
seldom viewed as biologically plausible models. This is
not a criticism of GAs, but rather a reflection of
choices made regarding the level of abstraction at
which biological mechanisms are modeled, and a
reflection of the more engineering-oriented goals of
the evolutionary computation community. Understanding
better and reducing this gap between GAs and genetics
has been a central issue in an interdisciplinary
project whose goal is to build GA-based computational
models of viral evolution. The result is a system
called Virtual Virus (VIV). The VIV incorporates a
number of more biologically plausible mechanisms,
including a more flexible genotype-to-phenotype
mapping. In VIV the genes are independent of position,
and genomes can vary in length and may contain
noncoding regions, as well as duplicative or competing
genes.
Initial computational studies with VIV have already
revealed several emergent phenomena of both biological
and computational interest. In the absence of any
penalty based on genome length, VIV develops
individuals with long genomes and also performs more
poorly (from a problem-solving viewpoint) than when a
length penalty is used. With a fixed linear length
penalty, genome length tends to increase dramatically
in the early phases of evolution and then decrease to a
level based on the mutation rate. The plateau genome
length (i.e., the average length of individuals in the
final population) generally increases in response to an
increase in the base mutation rate. When VIV converges,
there tend to be many copies of good alternative genes
within the individuals. We observed many instances of
switching between active and inactive genes during the
entire evolutionary process. These observations support
the conclusion that noncoding regions serve a positive
step in understanding how GAs might exploit more of the
power and flexibility of biological evolution while
simultaneously providing better tools for understanding
evolving biological systems.
Evolutionary Computation (Journal)
Special Issue: Variable-Length Representation and
Noncoding Segments for Evolutionary Algorithms Edited
by Annie S. Wu and Wolfgang Banzhaf
%0 Journal Article
%1 burk:1998:pmgGA
%A Burke, Donald S.
%A De Jong, Kenneth A.
%A Grefenstette, John J.
%A Ramsey, Connie Loggia
%A Wu, Annie S.
%D 1998
%J Evolutionary Computation
%K Models adaptation, algorithms, duplicative evolution, functions, genes genetic genome length noncoding of penalty regions, representation, variable-length viral
%N 4
%P 387--410
%R doi:10.1162/evco.1998.6.4.387
%T Putting More Genetics into Genetic Algorithms
%U http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.387
%V 6
%X The majority of current genetic algorithms (GAs),
while inspired by natural evolutionary systems, are
seldom viewed as biologically plausible models. This is
not a criticism of GAs, but rather a reflection of
choices made regarding the level of abstraction at
which biological mechanisms are modeled, and a
reflection of the more engineering-oriented goals of
the evolutionary computation community. Understanding
better and reducing this gap between GAs and genetics
has been a central issue in an interdisciplinary
project whose goal is to build GA-based computational
models of viral evolution. The result is a system
called Virtual Virus (VIV). The VIV incorporates a
number of more biologically plausible mechanisms,
including a more flexible genotype-to-phenotype
mapping. In VIV the genes are independent of position,
and genomes can vary in length and may contain
noncoding regions, as well as duplicative or competing
genes.
Initial computational studies with VIV have already
revealed several emergent phenomena of both biological
and computational interest. In the absence of any
penalty based on genome length, VIV develops
individuals with long genomes and also performs more
poorly (from a problem-solving viewpoint) than when a
length penalty is used. With a fixed linear length
penalty, genome length tends to increase dramatically
in the early phases of evolution and then decrease to a
level based on the mutation rate. The plateau genome
length (i.e., the average length of individuals in the
final population) generally increases in response to an
increase in the base mutation rate. When VIV converges,
there tend to be many copies of good alternative genes
within the individuals. We observed many instances of
switching between active and inactive genes during the
entire evolutionary process. These observations support
the conclusion that noncoding regions serve a positive
step in understanding how GAs might exploit more of the
power and flexibility of biological evolution while
simultaneously providing better tools for understanding
evolving biological systems.
@article{burk:1998:pmgGA,
abstract = {The majority of current genetic algorithms (GAs),
while inspired by natural evolutionary systems, are
seldom viewed as biologically plausible models. This is
not a criticism of GAs, but rather a reflection of
choices made regarding the level of abstraction at
which biological mechanisms are modeled, and a
reflection of the more engineering-oriented goals of
the evolutionary computation community. Understanding
better and reducing this gap between GAs and genetics
has been a central issue in an interdisciplinary
project whose goal is to build GA-based computational
models of viral evolution. The result is a system
called Virtual Virus (VIV). The VIV incorporates a
number of more biologically plausible mechanisms,
including a more flexible genotype-to-phenotype
mapping. In VIV the genes are independent of position,
and genomes can vary in length and may contain
noncoding regions, as well as duplicative or competing
genes.
Initial computational studies with VIV have already
revealed several emergent phenomena of both biological
and computational interest. In the absence of any
penalty based on genome length, VIV develops
individuals with long genomes and also performs more
poorly (from a problem-solving viewpoint) than when a
length penalty is used. With a fixed linear length
penalty, genome length tends to increase dramatically
in the early phases of evolution and then decrease to a
level based on the mutation rate. The plateau genome
length (i.e., the average length of individuals in the
final population) generally increases in response to an
increase in the base mutation rate. When VIV converges,
there tend to be many copies of good alternative genes
within the individuals. We observed many instances of
switching between active and inactive genes during the
entire evolutionary process. These observations support
the conclusion that noncoding regions serve a positive
step in understanding how GAs might exploit more of the
power and flexibility of biological evolution while
simultaneously providing better tools for understanding
evolving biological systems.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Burke, Donald S. and {De Jong}, Kenneth A. and Grefenstette, John J. and Ramsey, Connie Loggia and Wu, Annie S.},
biburl = {https://www.bibsonomy.org/bibtex/25c974747a3e00e2e6b5e975f7c5a4f1c/brazovayeye},
doi = {doi:10.1162/evco.1998.6.4.387},
interhash = {3dd80dc939d2285e5225c5b35b9c4924},
intrahash = {5c974747a3e00e2e6b5e975f7c5a4f1c},
journal = {Evolutionary Computation},
keywords = {Models adaptation, algorithms, duplicative evolution, functions, genes genetic genome length noncoding of penalty regions, representation, variable-length viral},
month = {Winter},
notes = {Evolutionary Computation (Journal)
Special Issue: Variable-Length Representation and
Noncoding Segments for Evolutionary Algorithms Edited
by Annie S. Wu and Wolfgang Banzhaf},
number = 4,
pages = {387--410},
size = {25 pages},
timestamp = {2008-06-19T17:37:08.000+0200},
title = {Putting More Genetics into Genetic Algorithms},
url = {http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.387},
volume = 6,
year = 1998
}