Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials

被引：0

作者：

Mohid, Maktuba ^{[1
]}

Miller, Julian Francis ^{[1
]}

Harding, Simon L. ^{[2
]}

Tufte, Gunnar ^{[2
]}

Lykkebø, Odd Rune ^{[3
]}

Massey, Mark K. ^{[3
]}

Petty, Michael C. ^{[3
]}

机构：

[1] Department of Electronics, University of York, York

[2] Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim

[3] School of Engineering and Computing Sciences and Centre for Molecular and Nanoscale Electronics, Durham University

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8672卷

关键词：

classification problem; evolution-in-materio; Evolutionary algorithm; evolvable hardware; machine learning; material computation;

D O I：

10.1007/978-3-319-10762-2_71

中图分类号：

学科分类号：

摘要：

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit the properties of physical matter to solve computational problems without requiring a detailed understanding of such properties. EIM has so far been applied to very few computational problems. 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 machine learning classification problems. This is the first time that EIM has been applied to such problems. We evaluate the approach on two standard datasets: Lenses and Iris. Comparing our technique with a well-known software-based evolutionary method indicates that EIM performs reasonably well. We suggest that EIM offers a promising new direction for evolutionary computation. © Springer International Publishing Switzerland 2014.

引用

页码：721 / 730

页数：9

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