This chapter provides an overview of evolutionary approaches to supervised learning. It starts with the definition and scope of the opportunity, and then reviews three main areas: evolving general neural network designs, evolving solutions that are explainable, and forming a synergy of evolutionary and gradient-based methods.

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In W. Banzhaf, P. Machado, and M. Zhang, editors, Handbook of Evolutionary Machine Learning, New York, 2023. Springer.

@incollection{miikkulainen:emlchapter23, title={Evolutionary Supervised Machine Learning}, author={Risto Miikkulainen}, booktitle={Handbook of Evolutionary Machine Learning}, month={ }, editor={W. Banzhaf and P. Machado and M. Zhang}, address={New York}, publisher={Springer}, url="http://nn.cs.utexas.edu/?miikkulainen:emlchapter23", year={2023} }