Tensorized NeuroEvolution of Augmenting Topologies: Accelerating Neuroevolution Algorithms with GPU-powered Parallel Processing

This paper introduces a tensorization method that transforms the diverse network topologies and associated operations in the NeuroEvolution of Augmenting Topologies (NEAT) algorithm into uniformly shaped tensors, enabling parallel processing across the entire population on GPUs. The authors develop TensorNEAT, a GPU-accelerated NEAT library that leverages this tensorization approach to achieve significant speedups compared to existing NEAT implementations.