Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms

Provides an in-depth guide to neuroevolution, a field focusing on evolving artificial neural networks, primarily using the NEAT algorithm and its advanced derivatives like HyperNEAT and ES-HyperNEAT. It covers practical applications such as solving the XOR problem, pole-balancing, and autonomous maze navigation, highlighting the effectiveness of Novelty Search optimization in deceptive landscapes. The text also explores deep neuroevolution for reinforcement learning in complex environments like Atari games, detailing CNN architectures and unique genome encoding schemes. Additionally, it offers best practices, coding tips, and performance metrics for those implementing neuroevolutionary algorithms.

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