Evolving Novel Gene Regulatory Networks for Structural Engineering Designs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8355
- @Article{Dubey:2024:AlifeJ,
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author = "Rahul Dubey and Simon Hickinbotham and
Andrew Colligan and Imelda Friel and Edgar Buchanan and Mark Price and
Andy M. Tyrrell",
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title = "Evolving Novel Gene Regulatory Networks for Structural
Engineering Designs",
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journal = "Artificial Life",
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year = "2024",
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volume = "30",
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number = "4",
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pages = "466--485",
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month = nov,
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keywords = "genetic algorithms, genetic programming, Evolutionary
search, gene regulatory networks, NEAT, CGP, design
optimisation",
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ISSN = "1064-5462",
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URL = "
https://pure.qub.ac.uk/en/publications/evolving-novel-gene-regulatory-networks-for-structural-engineerin",
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DOI = "
doi:10.1162/artl_a_00448",
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abstract = "Engineering design optimisation poses a significant
challenge, usually requiring human expertise to
discover superior solutions. Although various search
techniques have been employed to generate diverse
designs, their effectiveness is often limited by
problem-specific parameter tuning, making them less
generalizable and scalable. This article introduces a
framework inspired by evolutionary and developmental
(evo-devo) concepts, aiming to automate the evolution
of structural engineering designs. In biological
systems, evo-devo governs the growth of single-cell
organisms into multicellular organisms through the use
of gene regulatory networks (GRNs). GRNs are inherently
complex and highly nonlinear, and this article explores
the use of neural networks and genetic programming as
artificial representations of GRNs to emulate such
behaviours. To evolve a wide range of Pareto fronts for
artificial GRNs, this article introduces a new
technique, a real value-encoded neuroevolutionary
method termed real-encoded NEAT (RNEAT). The
performance of RNEAT is compared with that of two
well-known evolutionary search techniques across
different 2-D and 3-D problems. The experimental
results demonstrate two key findings. First, the
proposed framework effectively generates a population
of GRNs that can produce diverse structures for both
2-D and 3-D problems. Second, the proposed RNEAT
algorithm outperforms its competitors on more than
50percent of the problems examined. These results
validate the proof of concept underlying the proposed
evo-devo-based engineering design evolution.",
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notes = "Also known as \cite{10791473}",
- }
Genetic Programming entries for
Rahul Dubey
Simon John Hickinbotham
Andrew Colligan
Imelda Friel
Edgar Buchanan
Mark Price
Andrew M Tyrrell
Citations