Jones, Benedict A. H. and Chouard, John L. P. and Branco, Bianca C. C. and Vissol-Gaudin, Eléonore G. B. and Pearson, Christopher and Petty, Michael C. and Al Moubayed, Noura and Zeze, Dagou A. and Groves, Chris (2022) 'Towards Intelligently Designed Evolvable Processors.', Evolutionary Computation, 30 (4). pp. 479-501.
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.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1162/evco_a_00309|
|Publisher statement:||This article has been accepted for publication in Evolutionary Computation.|
|Date accepted:||09 March 2022|
|Date deposited:||09 March 2022|
|Date of first online publication:||12 August 2022|
|Date first made open access:||09 March 2022|
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