Outputs (5)

In-Materio Extreme Learning Machines (2022)
Book Chapter
Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as... Read More about In-Materio Extreme Learning Machines.

Towards Intelligently Designed Evolvable Processors (2022)
Journal Article
Jones, B. A., Chouard, J. L., Branco, B. C., Vissol-Gaudin, E. G., Pearson, C., Petty, M. C., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). Towards Intelligently Designed Evolvable Processors. Evolutionary Computation, 30(4), 479-501. https://doi.org/10.1162/evco_a_00309

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 pro... Read More about Towards Intelligently Designed Evolvable Processors.

Enhanced Methods for Evolution in-Materio Processors (2022)
Presentation / Conference Contribution
Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2023, November). Enhanced Methods for Evolution in-Materio Processors. Presented at IEEE International Conference on Rebooting Computing (ICRC 2021), Virtual

Evolution-in-Materio (EiM) is an unconventional computing paradigm, which uses an Evolutionary Algorithm (EA) to configure a material's parameters so that it can perform a computational task. While EiM processors show promise, slow manufacturing and... Read More about Enhanced Methods for Evolution in-Materio Processors.

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018, July). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. Presented at 2018 IEEE World Congress on Computational Intelligence (WCCI 2018)., Rio de Janeiro, Brazil

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.

Evolution of Electronic Circuits using Carbon Nanotube Composites (2016)
Journal Article
Massey, M., Kotsialos, A., Volpati, D., Vissol-Gaudin, E., Pearson, C., Bowen, L., …Petty, M. (2016). Evolution of Electronic Circuits using Carbon Nanotube Composites. Scientific Reports, 6, Article 32197. https://doi.org/10.1038/srep32197

Evolution-in-materio concerns the computer controlled manipulation of material systems using external stimuli to train or evolve the material to perform a useful function. In this paper we demonstrate the evolution of a disordered composite material,... Read More about Evolution of Electronic Circuits using Carbon Nanotube Composites.