Journal
EVOLUTIONARY COMPUTATION
Volume 25, Issue 4, Pages 643-671Publisher
MIT PRESS
DOI: 10.1162/evco_a_00197
Keywords
Unconventional computation; evolutionary computation; classification; mutual information; photosensitive Belousov-Zhabotinsky reaction; droplet; information-theory-based fitness
Funding
- NEUNEU project - European Community [FP7-ICT-2009-4 ICT-4-8.3, 248992]
- Polish Ministry of Science [2187/7.PRUE/ 2011/2]
- Foundation for Polish Science [MPD/2009/1/styp14]
- Polish National Science Centre [UMO-2014/15/B/ST4/04954]
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Unconventional computing devices operating on nonlinear chemical media offer an interesting alternative to standard, semiconductor-based computers. In this work we study in-silico a chemical medium composed of communicating droplets that functions as a database classifier. The droplet network can be programmed by an externally provided illumination pattern. The complex relationship between the illumination pattern and the droplet behavior makes manual programming hard. We introduce an evolutionary algorithm that automatically finds the optimal illumination pattern for a given classification problem. Notably, our approach does not require us to prespecify the signals that represent the output classes of the classification problem, which is achieved by using a fitness function that measures the mutual information between chemical oscillation patterns and desired output classes. We illustrate the feasibility of our approach in computer simulations by evolving droplet classifiers for three machine learning datasets. We demonstrate that the same medium composed of 25 droplets located on a square lattice can be successfully used for different classification tasks by applying different illumination patterns as its externally supplied program.
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