Benedict Jones benedict.jones@durham.ac.uk
PGR Student Doctor of Philosophy
Towards Intelligently Designed Evolvable Processors
Jones, Benedict A.H.; Chouard, John L.P.; Branco, Bianca C.C.; Vissol-Gaudin, Eléonore G.B.; Pearson, Christopher; Petty, Michael C.; Al Moubayed, Noura; Zeze, Dagou A.; Groves, Chris
Authors
John L.P. Chouard
Bianca C.C. Branco
Eleonore Vissol-Gaudin eleonore.vissol-gaudin@durham.ac.uk
PGR Student Doctor of Philosophy
Christopher Pearson
Michael C. Petty
Dr Noura Al Moubayed noura.al-moubayed@durham.ac.uk
Associate Professor
Professor Dagou Zeze d.a.zeze@durham.ac.uk
Professor
Professor Chris Groves chris.groves@durham.ac.uk
Professor
Abstract
Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material’s properties to achieve a specific computational function. This paper addresses the question of how successful and well performing Evolution-in-Materio processors can be designed through the selection of nanomaterials and an evolutionary algorithm for a target application. A physical model of a nanomaterial network is developed which allows for both randomness, and the possibility of Ohmic and non- Ohmic conduction, that are characteristic of such materials. These differing networks are then exploited by differential evolution, which optimises several configuration parameters (e.g., configuration voltages, weights, etc.), to solve different classification problems. We show that ideal nanomaterial choice depends upon problem complexity, with more complex problems being favoured by complex voltage dependence of conductivity and vice versa. Furthermore, we highlight how intrinsic nanomaterial electrical properties can be exploited by differing configuration parameters, clarifying the role and limitations of these techniques. These findings provide guidance for the rational design of nanomaterials and algorithms for future Evolution-in-Materio processors.
Citation
Jones, B. A., Chouard, J. L., Branco, B. C., Vissol-Gaudin, E. G., Pearson, C., Petty, M. C., …Groves, C. (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation, 30(4), 479-501. https://doi.org/10.1162/evco_a_00309
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 9, 2022 |
Online Publication Date | Aug 12, 2022 |
Publication Date | 2022-12 |
Deposit Date | Mar 9, 2022 |
Publicly Available Date | Mar 9, 2022 |
Journal | Evolutionary Computation |
Print ISSN | 1063-6560 |
Electronic ISSN | 1530-9304 |
Publisher | Massachusetts Institute of Technology Press |
Peer Reviewed | Peer Reviewed |
Volume | 30 |
Issue | 4 |
Pages | 479-501 |
DOI | https://doi.org/10.1162/evco_a_00309 |
Files
Accepted Journal Article
(5.9 Mb)
PDF
Copyright Statement
This article has been accepted for publication in Evolutionary Computation.
You might also like
Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention
(2022)
Conference Proceeding
Towards Graph Representation Learning Based Surgical Workflow Anticipation
(2022)
Conference Proceeding
Does lossy image compression affect racial bias within face recognition?
(2022)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search