Artificial Neural Networks (ANNs) are computer models that draw inspiration from the brain, where interconnected artificial neurons execute tasks such as image classification. Deep Neural Networks (DNNs), characterized by numerous layers of neurons, represent the cutting edge in tackling complex problems. While they can be manually designed, this requires specialized knowledge. Consequently, researchers have been exploring the automatic construction of ANNs, a field known as neuroevolution. This raises the question: Can DNNs be built automatically, without manual intervention, and still effectively solve problems? To address this, we will employ evolutionary algorithms. Inspired by the theory of evolution, these algorithms encode DNNs as individuals in a population, seeking the most effective ones for problem-solving. Given the diversity of individuals (DNNs) within a population, we aim to automatically discover DNNs with novel architectures and configurations that are not yet documented in the literature.