A Chemical System that Recognizes the Shape of a Sphere

Abstract

Unconventional computing devices operating on nonlinear chemical media offer an interesting alternative to standard, semiconductor-based computers. In this work we consider database classifiers formed of interacting droplets in which a photosensitive variant of Belousov-Zhabotinsky (BZ) reaction proceeds. We introduce an evolutionary algorithm that searches for optimal construction of a droplets-based classifier for a given problem. The algorithm is based on maximizing the mutual information between the database and the observed evolution of medium. As an example application of chemical database classifiers we apply the idea to the dataset of points belonging to a unit cube. The dataset contains two output classes: 1 for points belonging to a sphere with radius 0.5 located in the cube center, and 0 for points outside of the sphere. The reliability of optimized chemical classifiers of such database for different numbers of droplets involved in data processing is presented.