Evolving design modifiers

Simon Hickinbotham; Rahul Dubey; Imelda Friel; Andrew Colligan; Mark Price; Andy Tyrrell

doi:10.1109/SSCI51031.2022.10022087

Evolving design modifiers

Simon Hickinbotham
, Rahul Dubey
, Imelda Friel
, Andrew Colligan
, Mark Price
, Andy Tyrrell

School of Mechanical and Aerospace Engineering

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

8 Citations (Scopus)

61 Downloads (Pure)

Abstract

Evolutionary Developmental biology (EvoDevo) is a process of directed growth whose mechanisms could be used in an evolutionary algorithm for engineering applications. Engineering design can be thought of as a search through a high-dimensional design space for a small number of solutions that are optimal by various metrics. Configuring this search within an EvoDevo algorithm may allow developmental processes to provide a facility to give more immediate, localised feedback to the system as it grows into its final optimal configuration (form). This approach would augment current design practices. The main components needed to run EvoDevo for engineering design are set out in this paper, and these are developed into an algorithm for initial investigations, resulting in evolved neural network-based structural design modifying operators that optimise the structure of a planar truss in an iterative, decentralized manner against multiple objectives. Preliminary results are presented which show that the two levels feedback at the Evo and Devo stages drive the system to ultimately produce feasible solutions.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI)
Editors	Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1052-1058
Number of pages	7
ISBN (Electronic)	9781665487689
ISBN (Print)	9781665487696
DOIs	https://doi.org/10.1109/SSCI51031.2022.10022087
Publication status	Published - 30 Jan 2023
Event	2022 IEEE Symposium Series on Computational Intelligence - Singapore, Singapore Duration: 04 Dec 2022 → 07 Dec 2022

Publication series

Name	Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)

Conference

Conference	2022 IEEE Symposium Series on Computational Intelligence
Abbreviated title	SSCI 2022
Country/Territory	Singapore
City	Singapore
Period	04/12/2022 → 07/12/2022

Bibliographical note

Funding Information:
This work was funded by EPSRC, UK, grant reference EP/V007335/1

Publisher Copyright:
© 2022 IEEE.

Keywords

evodevo
generative design
genetic algorithms
neural networks
structural engineering

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Decision Sciences (miscellaneous)
Computational Mathematics
Control and Optimization
Transportation

Access to Document

10.1109/SSCI51031.2022.10022087Licence: Unspecified

Evolving design modifiers
Copyright 2023, IEEE. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 1.11 MBLicence: Unspecified

Cite this

Hickinbotham, S., Dubey, R., Friel, I., Colligan, A., Price, M., & Tyrrell, A. (2023). Evolving design modifiers. In H. Ishibuchi, C.-K. Kwoh, A.-H. Tan, D. Srinivasan, C. Miao, A. Trivedi, & K. Crockett (Eds.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1052-1058). (Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI51031.2022.10022087

Hickinbotham, Simon ; Dubey, Rahul ; Friel, Imelda et al. / Evolving design modifiers. Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI). editor / Hisao Ishibuchi ; Chee-Keong Kwoh ; Ah-Hwee Tan ; Dipti Srinivasan ; Chunyan Miao ; Anupam Trivedi ; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1052-1058 (Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)).

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title = "Evolving design modifiers",

abstract = "Evolutionary Developmental biology (EvoDevo) is a process of directed growth whose mechanisms could be used in an evolutionary algorithm for engineering applications. Engineering design can be thought of as a search through a high-dimensional design space for a small number of solutions that are optimal by various metrics. Configuring this search within an EvoDevo algorithm may allow developmental processes to provide a facility to give more immediate, localised feedback to the system as it grows into its final optimal configuration (form). This approach would augment current design practices. The main components needed to run EvoDevo for engineering design are set out in this paper, and these are developed into an algorithm for initial investigations, resulting in evolved neural network-based structural design modifying operators that optimise the structure of a planar truss in an iterative, decentralized manner against multiple objectives. Preliminary results are presented which show that the two levels feedback at the Evo and Devo stages drive the system to ultimately produce feasible solutions.",

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note = "Funding Information: This work was funded by EPSRC, UK, grant reference EP/V007335/1 Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 ; Conference date: 04-12-2022 Through 07-12-2022",

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Hickinbotham, S, Dubey, R, Friel, I, Colligan, A, Price, M & Tyrrell, A 2023, Evolving design modifiers. in H Ishibuchi, C-K Kwoh, A-H Tan, D Srinivasan, C Miao, A Trivedi & K Crockett (eds), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI). Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Institute of Electrical and Electronics Engineers Inc., pp. 1052-1058, 2022 IEEE Symposium Series on Computational Intelligence, Singapore, Singapore, 04/12/2022. https://doi.org/10.1109/SSCI51031.2022.10022087

Evolving design modifiers. / Hickinbotham, Simon; Dubey, Rahul; Friel, Imelda et al.
Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI). ed. / Hisao Ishibuchi; Chee-Keong Kwoh; Ah-Hwee Tan; Dipti Srinivasan; Chunyan Miao; Anupam Trivedi; Keeley Crockett. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1052-1058 (Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)).

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

TY - GEN

T1 - Evolving design modifiers

AU - Hickinbotham, Simon

AU - Dubey, Rahul

AU - Friel, Imelda

AU - Colligan, Andrew

AU - Price, Mark

AU - Tyrrell, Andy

PY - 2023/1/30

Y1 - 2023/1/30

N2 - Evolutionary Developmental biology (EvoDevo) is a process of directed growth whose mechanisms could be used in an evolutionary algorithm for engineering applications. Engineering design can be thought of as a search through a high-dimensional design space for a small number of solutions that are optimal by various metrics. Configuring this search within an EvoDevo algorithm may allow developmental processes to provide a facility to give more immediate, localised feedback to the system as it grows into its final optimal configuration (form). This approach would augment current design practices. The main components needed to run EvoDevo for engineering design are set out in this paper, and these are developed into an algorithm for initial investigations, resulting in evolved neural network-based structural design modifying operators that optimise the structure of a planar truss in an iterative, decentralized manner against multiple objectives. Preliminary results are presented which show that the two levels feedback at the Evo and Devo stages drive the system to ultimately produce feasible solutions.

AB - Evolutionary Developmental biology (EvoDevo) is a process of directed growth whose mechanisms could be used in an evolutionary algorithm for engineering applications. Engineering design can be thought of as a search through a high-dimensional design space for a small number of solutions that are optimal by various metrics. Configuring this search within an EvoDevo algorithm may allow developmental processes to provide a facility to give more immediate, localised feedback to the system as it grows into its final optimal configuration (form). This approach would augment current design practices. The main components needed to run EvoDevo for engineering design are set out in this paper, and these are developed into an algorithm for initial investigations, resulting in evolved neural network-based structural design modifying operators that optimise the structure of a planar truss in an iterative, decentralized manner against multiple objectives. Preliminary results are presented which show that the two levels feedback at the Evo and Devo stages drive the system to ultimately produce feasible solutions.

KW - evodevo

KW - generative design

KW - genetic algorithms

KW - neural networks

KW - structural engineering

U2 - 10.1109/SSCI51031.2022.10022087

DO - 10.1109/SSCI51031.2022.10022087

M3 - Conference contribution

AN - SCOPUS:85147799849

SN - 9781665487696

T3 - Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)

SP - 1052

EP - 1058

BT - Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI)

A2 - Ishibuchi, Hisao

A2 - Kwoh, Chee-Keong

A2 - Tan, Ah-Hwee

A2 - Srinivasan, Dipti

A2 - Miao, Chunyan

A2 - Trivedi, Anupam

A2 - Crockett, Keeley

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE Symposium Series on Computational Intelligence

Y2 - 4 December 2022 through 7 December 2022

ER -

Hickinbotham S, Dubey R, Friel I, Colligan A, Price M, Tyrrell A. Evolving design modifiers. In Ishibuchi H, Kwoh CK, Tan AH, Srinivasan D, Miao C, Trivedi A, Crockett K, editors, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI). Institute of Electrical and Electronics Engineers Inc. 2023. p. 1052-1058. (Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)). doi: 10.1109/SSCI51031.2022.10022087

Evolving design modifiers

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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