Neuroevolution, or optimization of neural networks through evolutionary computation, has been a growing subarea of machine learning since the 1990s. Its primary focus is on evolving neural networks for intelligent agents when the training targets are not known, and good performance requires many decisions over time, such as robotic control, game playing, and decision-making. More recently it has also been extended to optimizing deep-learning architectures, understanding how biological intelligence evolved, and optimizing neural networks for hardware implementation. This book introduces students to the basics of neuroevolution, progresses to several advanced topics that make neuroevolution more effective and more general, reviews example application areas, and proposes further research questions. Hands-on experience is provided through a Python-based software platform with animations, interactive demos, exercises, and project environments. Book website

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To Appear In , Cambridge, MA, 2025. MIT Press.

@book{risi:book25, title={Neuroevolution: Harnessing Creativity in AI Model Design}, author={Sebastian Risi and David Ha and Yujin Tang and Risto Miikkulainen}, month={ }, address={Cambridge, MA}, publisher={MIT Press}, url="http://nn.cs.utexas.edu/?risi:book25", year={2025} }

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