Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akbarzadeh, V., Sadeghian, A., dos Santos, M.: Derivation of relational fuzzy classification rules using evolutionary computation. In: IEEE Int. Conf. on Fuzzy Systems, pp. 1689–1693 (2008)
Bache, K., Lichman, M.: UCI machine learning repository (2013), http://archive.ics.uci.edu/ml
Broersma, H., Gomez, F., Miller, J.F., Petty, M., Tufte, G.: Nascence project: Nanoscale engineering for novel computation using evolution. International Journal of Unconventional Computing 8(4), 313–317 (2012)
Harding, S., Miller, J.F.: Evolution in materio: A tone discriminator in liquid crystal. In: Proc. Congress on Evolutionary Computation 2004, vol. 2, pp. 1800–1807 (2004)
Harding, S., Miller, J.F.: Evolution in materio: A real time robot controller in liquid crystal. In: Proc. NASA/DoD Conference on Evolvable Hardware, pp. 229–238 (2005)
Harding, S.L., Miller, J.F.: Evolution in materio: Evolving logic gates in liquid crystal. Int. J. of Unconventional Computing 3(4), 243–257 (2007)
Leitner, J., Harding, S., Forster, A., Schmidhuber, J.: Mars terrain image classification using cartesian genetic programming. In: 11th International Symposium on Artificial Intelligence, Robotics and Automation in Space, i-SAIRAS (2012)
Lykkebø, O.R., Harding, S., Tufte, G., Miller, J.F.: Mecobo: A Hardware and Software Platform for In Materio Evolution. In: Ibarra, O.H., Kari, L., Kopecki, S. (eds.) UCNC 2014. LNCS, vol. 8553, pp. 267–279. Springer, Heidelberg (2014), http://dx.doi.org/10.1007/978-3-319-08123-6_22
Miller, J.F. (ed.): Cartesian Genetic Programming. Springer (2011)
Miller, J.F., Downing, K.: Evolution in materio: Looking beyond the silicon box. In: Stoica, A., Lohn, J., Katz, R., Keymeulen, D., Zebulum, R.S. (eds.) The 2002 NASA/DoD Conference on Evolvable Hardware, vol. 7, pp. 167–176. IEEE Computer Society (2002)
Miller, J.F., Harding, S.L., Tufte, G.: Evolution-in-materio: evolving computation in materials. Evolutionary Intelligence 7, 49–67 (2014)
Thompson, A.: Hardware Evolution - Automatic Design of Electronic Circuits in Reconfigurable Hardware by Artificial Evolution. Springer (1998)
Völk, K., Miller, J.F., Smith, S.L.: Multiple network CGP for the classification of mammograms. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 405–413. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mohid, M. et al. (2014). Evolution-In-Materio: Solving Machine Learning Classification Problems Using Materials. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_71
Download citation
DOI: https://doi.org/10.1007/978-3-319-10762-2_71
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10761-5
Online ISBN: 978-3-319-10762-2
eBook Packages: Computer ScienceComputer Science (R0)