Evolving Design for Engineering Structures

Rahul Dubey; Simon John Hickinbotham; Edgar Buchanan Berumen; Andrew Colligan; Imelda Friel; Mark Price; Andy Tyrrell

doi:10.1109/CEC60901.2024.10611851

Evolving Design for Engineering Structures

Rahul Dubey, Simon John Hickinbotham, Edgar Buchanan Berumen, Andrew Colligan, Imelda Friel, Mark Price, Andy Tyrrell

Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In recent years, the evolutionary developmental (Evo- Devo) concept has gained traction in the field of engineering design. This paper presents a new biologically inspired approach rooted in Evo- Devo principles to iteratively develop car chassis designs based on a specified design brief. The proposed method draws inspiration from biological cell growth and differentiation behaviors to generate intricate engineering designs. Employing evolutionary algorithms, the paper aims to evolve gene regulatory networks that govern the growth of a minimal viable design. The primary goal is to achieve an optimal design capable of withstanding sudden crash impacts within safety limits. Comprehensive simulation results demonstrate that the proposed approach, using genetic algorithms, evolves gene regulatory networks that generate a spectrum of viable designs. Furthermore, the best-evolved solution exhibits generalizability and adaptability across different simulation parameters.

Original language	English
Title of host publication	2024 IEEE Congress on Evolutionary Computation (CEC)
Publisher	IEEE
Pages	1-8
Number of pages	8
ISBN (Electronic)	979-8-3503-0836-5
DOIs	https://doi.org/10.1109/CEC60901.2024.10611851
Publication status	Published - 5 Jul 2024

Keywords

neural networks
GRN
engineering design
GENETIC ALGORITHMS

Access to Document

10.1109/CEC60901.2024.10611851

Cite this

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title = "Evolving Design for Engineering Structures",

abstract = "In recent years, the evolutionary developmental (Evo- Devo) concept has gained traction in the field of engineering design. This paper presents a new biologically inspired approach rooted in Evo- Devo principles to iteratively develop car chassis designs based on a specified design brief. The proposed method draws inspiration from biological cell growth and differentiation behaviors to generate intricate engineering designs. Employing evolutionary algorithms, the paper aims to evolve gene regulatory networks that govern the growth of a minimal viable design. The primary goal is to achieve an optimal design capable of withstanding sudden crash impacts within safety limits. Comprehensive simulation results demonstrate that the proposed approach, using genetic algorithms, evolves gene regulatory networks that generate a spectrum of viable designs. Furthermore, the best-evolved solution exhibits generalizability and adaptability across different simulation parameters.",

keywords = "neural networks, GRN, engineering design, GENETIC ALGORITHMS",

author = "Rahul Dubey and Hickinbotham, {Simon John} and {Buchanan Berumen}, Edgar and Andrew Colligan and Imelda Friel and Mark Price and Andy Tyrrell",

year = "2024",

month = jul,

day = "5",

doi = "10.1109/CEC60901.2024.10611851",

language = "English",

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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",

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TY - GEN

T1 - Evolving Design for Engineering Structures

AU - Dubey, Rahul

AU - Hickinbotham, Simon John

AU - Buchanan Berumen, Edgar

AU - Colligan, Andrew

AU - Friel, Imelda

AU - Price, Mark

AU - Tyrrell, Andy

PY - 2024/7/5

Y1 - 2024/7/5

N2 - In recent years, the evolutionary developmental (Evo- Devo) concept has gained traction in the field of engineering design. This paper presents a new biologically inspired approach rooted in Evo- Devo principles to iteratively develop car chassis designs based on a specified design brief. The proposed method draws inspiration from biological cell growth and differentiation behaviors to generate intricate engineering designs. Employing evolutionary algorithms, the paper aims to evolve gene regulatory networks that govern the growth of a minimal viable design. The primary goal is to achieve an optimal design capable of withstanding sudden crash impacts within safety limits. Comprehensive simulation results demonstrate that the proposed approach, using genetic algorithms, evolves gene regulatory networks that generate a spectrum of viable designs. Furthermore, the best-evolved solution exhibits generalizability and adaptability across different simulation parameters.

AB - In recent years, the evolutionary developmental (Evo- Devo) concept has gained traction in the field of engineering design. This paper presents a new biologically inspired approach rooted in Evo- Devo principles to iteratively develop car chassis designs based on a specified design brief. The proposed method draws inspiration from biological cell growth and differentiation behaviors to generate intricate engineering designs. Employing evolutionary algorithms, the paper aims to evolve gene regulatory networks that govern the growth of a minimal viable design. The primary goal is to achieve an optimal design capable of withstanding sudden crash impacts within safety limits. Comprehensive simulation results demonstrate that the proposed approach, using genetic algorithms, evolves gene regulatory networks that generate a spectrum of viable designs. Furthermore, the best-evolved solution exhibits generalizability and adaptability across different simulation parameters.

KW - neural networks

KW - GRN

KW - engineering design

KW - GENETIC ALGORITHMS

U2 - 10.1109/CEC60901.2024.10611851

DO - 10.1109/CEC60901.2024.10611851

M3 - Conference contribution

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BT - 2024 IEEE Congress on Evolutionary Computation (CEC)

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