Parameter Optimization of Deep Learning Models by Evolutionary Algorithms

Abstract

Deep learning is a very popular gradient based search technique nowadays. In this field of machine learning we usually apply neural networks with various structure. The algorithms of the deep learning techniques and the structure of the applied networks have several parameters that have a huge impact on the performance of the search technique. These parameters are called hyperparameters. The aim of our current research is to optimize these hyperparameters using evolutionary and swarm based optimization algorithms.