Evolving Novel Gene Regulatory Networks for Structural Engineering Designs

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Dubey:2024:AlifeJ,

• author = "Rahul Dubey and Simon Hickinbotham and Andrew Colligan and Imelda Friel and Edgar Buchanan and Mark Price and Andy M. Tyrrell",
• title = "Evolving Novel Gene Regulatory Networks for Structural Engineering Designs",
• journal = "Artificial Life",
• year = "2024",
• volume = "30",
• number = "4",
• pages = "466--485",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Evolutionary search, gene regulatory networks, NEAT, CGP, design optimisation",
• ISSN = "1064-5462",
• URL = "https://pure.qub.ac.uk/en/publications/evolving-novel-gene-regulatory-networks-for-structural-engineerin",
• DOI = "doi:10.1162/artl_a_00448",
• 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.",
• 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