Problem Being Addressed:
Deep learning convolutional neural networks require extensive manual trial and error with expensive resources to find an optimal parameter set.
Implement a genetic algorithm to quickly find optimal parameters & hyperparameters for deep learning convolutional neural networks.
Our team implemented a genetic algorithm and specific techniques for different stages such as cross over, mutation, fitness function, & more and integrated this with Deep Learning to produce optimal CNN models for a given dataset such that the new architecture and parameters (example - filters, activation, optimizer, pool size etc.) will result in optimal accuracy and loss.