  One of the most important properties of deep auto-encoders (DAEs) is their
capability to extract high level features from row data. Hence, especially
recently, the autoencoders are preferred to be used in various classification
problems such as image and voice recognition, computer security, medical data
analysis, etc. Despite, its popularity and high performance, the training phase
of autoencoders is still a challenging task, involving to select best
parameters that let the model to approach optimal results. Different training
approaches are applied to train sparse autoencoders. Previous studies and
preliminary experiments reveal that those approaches may present remarkable
results in same problems but also disappointing results can be obtained in
other complex problems. Metaheuristic algorithms have emerged over the last two
decades and are becoming an essential part of contemporary optimization
techniques. Gray wolf optimization (GWO) is one of the current of those
algorithms and is applied to train sparse auto-encoders for this study. This
model is validated by employing several popular Gene expression databases.
Results are compared with previous state-of-the art methods studied with the
same data sets and also are compared with other popular metaheuristic
algorithms, namely, Genetic Algorithms (GA), Particle Swarm Optimization (PSO)
and Artificial Bee Colony (ABC). Results reveal that the performance of the
trained model using GWO outperforms on both conventional models and models
trained with most popular metaheuristic algorithms.
