We have published the paper entitled Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms in the Sensors journal: Q1 – Impact factor 3.576 (2020).
In this paper, we discuss the optimization of Deep Learning architectures using evolutionary algorithms. To this end, we propose a configurable optimization framework that is capable of modifying the architecture by including or removing layers from the initial solutions as well as optimizing hyperparameters.