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Applications of Information Theory Methods for Evolutionary Optimization of Chemical Computers

Institute of Physical Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 01-224 Warsaw, Poland
Entropy 2020, 22(3), 313; https://doi.org/10.3390/e22030313
Received: 15 February 2020 / Revised: 6 March 2020 / Accepted: 8 March 2020 / Published: 10 March 2020
It is commonly believed that information processing in living organisms is based on chemical reactions. However, the human achievements in constructing chemical information processing devices demonstrate that it is difficult to design such devices using the bottom-up strategy. Here I discuss the alternative top-down design of a network of chemical oscillators that performs a selected computing task. As an example, I consider a simple network of interacting chemical oscillators that operates as a comparator of two real numbers. The information on which of the two numbers is larger is coded in the number of excitations observed on oscillators forming the network. The parameters of the network are optimized to perform this function with the maximum accuracy. I discuss how information theory methods can be applied to obtain the optimum computing structure. View Full-Text
Keywords: chemical computing; oscillatory reaction; genetic optimization; classification problem; interacting oscillators chemical computing; oscillatory reaction; genetic optimization; classification problem; interacting oscillators
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Gorecki, J. Applications of Information Theory Methods for Evolutionary Optimization of Chemical Computers. Entropy 2020, 22, 313.

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