  In this paper, we continue the discussion on database classifiers constructed
with networks of interacting chemical oscillators. In our previous papers we
demonstrated that a small, regular network of oscillators can predict if three
random numbers in the range $[0,1]$ describe a point located inside a sphere
inscribed within the unit cube $[0,1] \times [0,1] \times [0,1]$ with the
accuracy exceeding $80 \%$. The parameters of the network were determined using
evolutionary optimization. Here we apply the same technique to investigate if
the classifier accuracy for this problem can be improved by selecting a
specific geometry of interacting oscillators. We also address questions on the
optimum size of the training database for evolutionary optimization and on the
minimum size of the testing dataset for objective evaluation of classifier
accuracy.
