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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.
Afiliación
  • Jones BAH; Department of Engineering, Durham University, Durham, DH1 3LE, UK benedict.jones@durham.ac.uk.
  • Chouard JLP; Department of Engineering, Durham University, Durham, DH1 3LE, UK john.chouard@gmail.com.
  • Branco BCC; Department of Engineering, Durham University, Durham, DH1 3LE, UK bicampanario@icloud.com.
  • Vissol-Gaudin EGB; Department of Engineering, Durham University, Durham, DH1 3LE, UK eleonore.vissol-gaudin@durham.ac.uk.
  • Pearson C; Department of Engineering, Durham University, Durham, DH1 3LE, UK christopher_pearson@icloud.com.
  • Petty MC; Department of Engineering, Durham University, Durham, DH1 3LE, UK m.c.petty@durham.ac.uk.
  • Al Moubayed N; Department of Computer Science, Durham University, Durham, DH1 3LE, UK noura.al-moubayed@durham.ac.uk.
  • Zeze DA; Department of Engineering, Durham University, Durham, DH1 3LE, UK d.a.zeze@durham.ac.uk.
  • Groves C; Department of Engineering, Durham University, Durham, DH1 3LE, UK chris.groves@durham.ac.uk.
Evol Comput ; 30(4): 479-501, 2022 Dec 01.
Article en En | MEDLINE | ID: mdl-35289840
Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material's properties to achieve a specific computational function. This article 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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos Tipo de estudio: Prognostic_studies Idioma: En Revista: Evol Comput Asunto de la revista: BIOLOGIA Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos Tipo de estudio: Prognostic_studies Idioma: En Revista: Evol Comput Asunto de la revista: BIOLOGIA Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos