  Deep neural network-based architectures give promising results in various
domains including pattern recognition. Finding the optimal combination of the
hyper-parameters of such a large-sized architecture is tedious and requires a
large number of laboratory experiments. But, identifying the optimal
combination of a hyper-parameter or appropriate kernel size for a given
architecture of deep learning is always a challenging and tedious task. Here,
we introduced a genetic algorithm-based technique to reduce the efforts of
finding the optimal combination of a hyper-parameter (kernel size) of a
convolutional neural network-based architecture. The method is evaluated on
three popular datasets of different handwritten Bangla characters and digits.
The implementation of the proposed methodology can be found in the following
link: https://github.com/DeepQn/GA-Based-Kernel-Size.
