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Evolution-In-Materio: Solving Computational Problems Using Materials

Mohid, Maktuba (2015) Evolution-In-Materio: Solving Computational Problems Using Materials. PhD thesis, University of York.

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Abstract

The motivation behind the research is to show that evolutionary algorithms can exploit properties of materials to solve various computational problems without requiring a detailed understanding of such properties. This approach is referred to as evolution-in-materio. In this research, it has been shown that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve a number of computational problems. Here it has been demonstrated for the first time that the evolution-in-materio method can be applied to function optimisation, machine learning classification, frequency classification, even parity and bin packing problems. This evolution-in-materio method has also been applied here to discriminate tones and control robots. The physical material used in each of these experiments is a mixture of single-walled carbon nanotubes and a polymer. This is the first time that such material has been used to solve computational problems. The results of all of these experiments indicate that evolution-in-materio has promise and further investigations would be fruitful. Other than the solutions regarding these computational problems, this thesis has also devised and investigated suitable input-output mappings and input signals that allow various computational problems to be solved using the Mecobo platform and the experimental material.

Item Type: Thesis (PhD)
Academic Units: The University of York > Electronics (York)
Identification Number/EthosID: uk.bl.ethos.668316
Depositing User: Mrs Maktuba Mohid
Date Deposited: 22 Oct 2015 15:28
Last Modified: 24 Jul 2018 15:21
URI: http://etheses.whiterose.ac.uk/id/eprint/10468

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