Outputs (7)

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., …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.

Electrical behaviour and evolutionary computation in thin films of bovine brain microtubules (2021)
Journal Article
Vissol-Gaudin, E., Pearson, C., Groves, C., Zeze, D. A., Cantiello, H. F., Cantero, M. D. R., & Petty, M. C. (2021). Electrical behaviour and evolutionary computation in thin films of bovine brain microtubules. Scientific Reports, 11, Article 10776. https://doi.org/10.1038/s41598-021-90260-0

We report on the electrical behaviour of thin films of bovine brain microtubules (MTs). For samples in both their dried and hydrated states, the measured currents reveal a power law dependence on the applied DC voltage. We attribute this to the injec... Read More about Electrical behaviour and evolutionary computation in thin films of bovine brain microtubules.

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Conference Proceeding
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. In 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings (646-653). https://doi.org/10.1109/cec.2018.8477779

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.

Computing Based on Material Training: Application to Binary Classification Problems (2017)
Conference Proceeding
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., & Petty, M. (2017). Computing Based on Material Training: Application to Binary Classification Problems. In 2017 IEEE International Conference on Rebooting Computing (ICRC) : 8-9 November 2017, Washington, DC, USA ; proceedings (274-281). https://doi.org/10.1109/icrc.2017.8123677

Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbon-nanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that... Read More about Computing Based on Material Training: Application to Binary Classification Problems.

Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms (2017)
Book Chapter
Vissol-Gaudin, E., Kotsialos, A., Massey, M. K., Groves, C., Pearson, C., Zeze, D. A., & Petty, M. C. (2017). Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms. In 2017 IEEE Congress on Evolutionary Computation (CEC) : 5-8 June 2017, Donostia-San Sebastián, Spain ; proceedings (1924-1931). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cec.2017.7969536

This paper presents a series of experiments demonstrating the capacity of single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained by evolutionary algorithms to act as classifiers on linear and nonlinear binary datasets. The t... Read More about Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms.

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms (2016)
Book Chapter
Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016). Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms. In J. Handl, E. Hart, P. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), Parallel problem solving from nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016 : proceedings (644-654). Springer Verlag. https://doi.org/10.1007/978-3-319-45823-6_60

The potential of Evolution in Materio (EiM) for machine learning problems is explored here. This technique makes use of evolutionary algorithms (EAs) to influence the processing abilities of an un-configured physically rich medium, via exploitation o... Read More about Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms (2016)
Conference Proceeding
Vissol-Gaudin, E., Kotsialos, A., Massey, M., Zeze, D., Pearson, C., Groves, C., & Petty, M. (2016). Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms. In M. Amos, & A. Condon (Eds.), Unconventional computation and natural computation : 15th International Conference, UCNC 2016, Manchester, UK, July 11-15, 2016 ; proceedings (130-141). https://doi.org/10.1007/978-3-319-41312-9_11

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.