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Professor Chris Groves' Outputs (3)

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.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms (2016)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016, July). Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms. Presented at 15th International Conference on Unconventional Computation and Natural Computation, Manchester, UK

Evolution-in-Materio uses evolutionary algorithms (EA) to exploit the physical properties of unconfigured, physically rich materials, in effect transforming them into information processors. The potential of this technique for machine learning proble... Read More about Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms.