Evolution-in-materio: solving computational problems using carbon nanotube–polymer composites

Mohid, M.; Miller, J.F.; Harding, S.L.; Tufte, G.; Massey, M.K.; Petty, M.C.

Abstract

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that using a purpose-built hardware platform called Mecobo, it is possible to solve computational problems by evolving voltages and signals applied to an electrode array covered with a carbon nanotube–polymer composite. We demonstrate for the first time that this methodology can be applied to function optimization and also to the tone discriminator problem (TDP). For function optimization, we evaluate the approach on a suite of optimization benchmarks and obtain results that in some cases come very close to the global optimum or are comparable with those obtained using well-known software-based evolutionary approach. We also obtain good results in comparison with prior work on the tone discriminator problem. In the case of the TDP we also investigated the relative merits of different mixtures of materials and organizations of electrode array.

Citation

Mohid, M., Miller, J., Harding, S., Tufte, G., Massey, M., & Petty, M. (2016). Evolution-in-materio: solving computational problems using carbon nanotube–polymer composites. Soft Computing, 20(8), 3007-3022. https://doi.org/10.1007/s00500-015-1928-6

• Journal Article Type: Article
• Acceptance Date: Nov 12, 2015
• Online Publication Date: Nov 12, 2015
• Publication Date: Aug 1, 2016
• Deposit Date: Dec 18, 2015
• Journal: Soft Computing
• Print ISSN: 1432-7643
• Electronic ISSN: 1433-7479
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 8
• Pages: 3007-3022
• DOI: https://doi.org/10.1007/s00500-015-1928-6
• Public URL: https://durham-repository.worktribe.com/output/1416438