Neuroevolution, or optimization of neural networks through evolutionary computation, is a method for constructing intelligent agents through population-based search. It is particularly useful in partially observable domains with sparse and multiobjective reinforcement; compared to other policy search techniques, its power comes from extensive exploration that allows it to find effective, often surprising solutions. Prime application domains include robotic control, game-playing agents, 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. Several synergies have also emerged with reinforcement learning and large language models. This tutorial introduces participants to the basics of neuroevolution, progresses to several advanced topics that make neuroevolution effective and general, reviews example application areas, and proposes further research questions. An optional hands-on exercise makes these concepts concrete and allows the participants to take advantage of neuroevolution immediately.
(1) Neuroevolution for control
(2) Evolutionary Model Merging
(3) Quality Diversity for Model Merging.
Instructions are given in the notebook.