  Convolutional Neural Networks (CNNs) have gained a significant attraction in
the recent years due to their increasing real-world applications. Their
performance is highly dependent to the network structure and the selected
optimization method for tuning the network parameters. In this paper, we
propose novel yet efficient methods for training convolutional neural networks.
The most of current state of the art learning method for CNNs are based on
Gradient decent. In contrary to the traditional CNN training methods, we
propose to optimize the CNNs using methods based on Genetic Algorithms (GAs).
These methods are carried out using three individual GA schemes, Steady-State,
Generational, and Elitism. We present new genetic operators for crossover,
mutation and also an innovative encoding paradigm of CNNs to chromosomes aiming
to reduce the resulting chromosome's size by a large factor. We compare the
effectiveness and scalability of our encoding with the traditional encoding.
Furthermore, the performance of individual GA schemes used for training the
networks were compared with each other in means of convergence rate and overall
accuracy. Finally, our new encoding alongside the superior GA-based training
scheme is compared to Backpropagation training with Adam optimization.
