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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

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Authors

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Benedict Jones benedict.jones@durham.ac.uk
PGR Student Doctor of Philosophy

John L.P. Chouard

Bianca C.C. Branco

Christopher Pearson

Michael C. Petty



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

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Copyright Statement
This article has been accepted for publication in Evolutionary Computation.







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