Department of Complex Systems, Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
Bio Systems Analysis Group, Institute of Computer Science, Friedrich Schiller University Jena, 07743 Jena, Germany
Evol Comput. 2017 Winter;25(4):643-671. doi: 10.1162/EVCO_a_00197. Epub 2016 Oct 11.
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.
基于非线性化学介质的非常规计算设备为标准的基于半导体的计算机提供了一种有趣的替代方案。在这项工作中，我们在计算机上研究了由相互通信的液滴组成的化学介质，该介质可用作数据库分类器。通过外部提供的照明模式可以对液滴网络进行“编程”。照明模式与液滴行为之间的复杂关系使得手动编程变得困难。我们引入了一种进化算法，可以自动找到给定分类问题的最佳照明模式。值得注意的是，我们的方法不需要预先指定表示分类问题输出类别的信号，而是通过使用一种衡量化学振荡模式与期望输出类之间的互信息的适应度函数来实现这一点。我们通过为三个机器学习数据集进化液滴分类器，在计算机模拟中说明了我们方法的可行性。我们证明，通过将不同的照明模式作为外部供应的程序应用于由位于正方形晶格上的 25 个液滴组成的相同介质，可以成功地用于不同的分类任务。
