Evolving Design Modifiers

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

Evolving Design Modifiers

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

Electronic Engineering

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

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	2022 IEEE Symposium Series on Computational Intelligence (SSCI)
Publication status	Published - 7 Dec 2022

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Evolving Design ModifiersFinal published version, 1.31 MBLicence: CC BY

Re-Imagining Engineering Design (REID): Growing Radical Cyber-Physical-Socio Phenotypes
Tyrrell, A.
EPSRC
1/05/21 → 30/04/26
Project: Research project (funded) › Research

Cite this

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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.",

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

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language = "English",

booktitle = "2022 IEEE Symposium Series on Computational Intelligence (SSCI)",

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T1 - Evolving Design Modifiers

AU - Hickinbotham, Simon John

AU - Dubey, Rahul

AU - Friel, Imelda

AU - Colligan, Andrew

AU - Price, Mark

AU - Tyrrell, Andy

PY - 2022/12/7

Y1 - 2022/12/7

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.

M3 - Conference contribution

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

ER -

Evolving Design Modifiers

Abstract

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Projects

Re-Imagining Engineering Design (REID): Growing Radical Cyber-Physical-Socio Phenotypes

Cite this