On the Advantages of Variable Length GRNs for the Evolution of Multicellular Developmental Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Trefzer:2012:ieeeTEC,
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author = "Martin A. Trefzer and Tuze Kuyucu and
Julian F. Miller and Andy M. Tyrrell",
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title = "On the Advantages of Variable Length GRNs for the
Evolution of Multicellular Developmental Systems",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2013",
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volume = "17",
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number = "1",
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pages = "100--121",
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month = feb,
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keywords = "genetic algorithms, genetic programming, GRN, gene
regulatory network",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2012.2185848",
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size = "22 pages",
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abstract = "Biological genomes have evolved over a period of
millions of years and comprise thousands of genes, even
for the simplest organisms. However, in nature, only
12percent of the genes play an active role in creating
and maintaining the organism, while the majority are
evolutionary fossils. This raises the question whether
a considerably larger number of (partly redundant)
genes are required in order to effectively build a
functional developmental system, of which, in the final
system only a fraction is required for the latter to
function. This paper investigates different approaches
to creating artificial developmental systems (ADSs)
based on variable length gene regulatory networks
(GRNs). The GRNs are optimised using an evolutionary
algorithm (EA). A comparison is made between the
different variable length representations and fixed
length representations. It is shown that variable
length GRNs can achieve both reducing computational
effort during optimisation and increasing speed and
compactness of the resulting ADS, despite the higher
complexity of the encoding required. The results may
also improve the understanding of how to effectively
model GRN based developmental systems. Taking results
of all experiments into account makes it possible to
create an overall ranking of the different patterns
used as a testbench in terms of their complexity. This
ranking may aid to compare related work against. In
addition this allows a detailed assessment of the ADS
used and enables the identification of missing
mechanisms.",
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notes = "also known as \cite{6151118}",
- }
Genetic Programming entries for
Martin A Trefzer
Tuze Kuyucu
Julian F Miller
Andrew M Tyrrell
Citations