Evolution-in-materio: solving function optimization problems using materials

Mohid, M; Miller, JF; Harding, SL; Tufte, G; Lykkebo, OR; Massey, MK; Petty, MC

Abstract

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve computational problems. We demonstrate for the first time that this methodology can be applied to function optimization. We evaluate the approach on 23 function optimization benchmarks and in some cases results come very close to the global optimum or even surpass those provided by a well-known software-based evolutionary approach. This indicates that EIM has promise and further investigations would be fruitful.

Citation

Mohid, M., Miller, J., Harding, S., Tufte, G., Lykkebo, O., Massey, M., & Petty, M. (2014, September). Evolution-in-materio: solving function optimization problems using materials. Presented at 2014 14th UK Workshop on Computational Intelligence (UKCI), Bradford, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2014 14th UK Workshop on Computational Intelligence (UKCI)
• Publication Date: Sep 1, 2014
• Deposit Date: Oct 30, 2014
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-8
• Book Title: Computational Intelligence (UKCI) : 2014 14th UK Workshop on, 8-10 September 2014, Bradford, UK ; proceedings.
• DOI: https://doi.org/10.1109/ukci.2014.6930152
• Keywords: Arrays, Benchmark testing, Biological cells, Electrodes, Hardware, Materials, Optimization, Evolution-in-materio, Evolutionary algorithm, Evolvable hardware, Function optimization, Material computation.
• Public URL: https://durham-repository.worktribe.com/output/1154744
• Additional Information: Conference dates: 8-10 Sept. 2014