Gaggle: Genetic Algorithms on the GPU using PyTorch

PyTorch has profoundly impacted the machine learning (ML) community by allowing researchers of all backgrounds to train models efficiently. While PyTorch is the de facto standard in ML, the evolutionary algorithms (EA) community instead relies on many different libraries, each with low adoption in practice. In an effort to provide a standardized library for EA, packages like LEAP and PyGAD have been developed. However, these libraries fall short in either scalability or usability. In particular, neither of these packages offers efficient support for neuroevolutionary tasks. We argue that the best way to develop a PyTorch-like library for EAs is to build on the already solid foundation of PyTorch itself. We present Gaggle, an efficient PyTorch-based EA library that better supports GPU-based tasks like neuroevolution while maintaining the efficiency of CPU-based problems. We evaluate Gaggle on various problems and find statistically significant improvements in runtime over prior work on problems like training neural networks. In addition to efficiency, Gaggle provides a simple single-line interface making it accessible to beginners and a more customizable research interface with detailed configuration files to better support the EA research community.