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Machine learning is a huge field of study in computer science and statisticsdedicated to the execution of computational tasks through algorithms that donot require explicit instructions but instead rely on learning patterns fromdata samples to automate inferences. A large portion of the work involved in amachine learning project is to define the best type of algorithm to solve agiven problem. Neural networks - especially deep neural networks - are thepredominant type of solution in the field. However, the networks themselves canproduce very different results according to the architectural choices made forthem. Finding the optimal network topology and configurations for a givenproblem is a challenge that requires domain knowledge and testing efforts dueto a large number of parameters that need to be considered. The purpose of thiswork is to propose an adapted implementation of a well-established evolutionarytechnique from the neuroevolution field that manages to automate the tasks oftopology and hyperparameter selection. It uses a popular and accessible machinelearning framework - Keras - as the back-end, presenting results and proposedchanges concerning the original algorithm. The implementation is available atGitHub (https://github.com/sbcblab/Keras-CoDeepNEAT) with documentation andexamples to reproduce the experiments performed for this work.

Neuroevolution of Neural Network Architectures Using CoDeepNEAT and Keras