Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations
Oskar Taubert, Marie Weiel, Daniel Coquelin, Anis Farshian, Charlotte Debus, Alexander H. Schug, Achim Streit, Markus Götz · Lecture notes in computer science · 2023
Abstract We present , an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, omits the synchronization after each generation as done in conventional genetic algorithms. Instead, it steers the search with the complete population present at time of breeding new individuals. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration and is easy to extend with custom functionality. We compare to the established optimization tool . We find that is up to three orders of magnitude faster without sacrificing solution accuracy, demonstrating the efficiency and efficacy of our lazy synchronization approach. Code and documentation are available at https://github.com/Helmholtz-AI-Energy/propulate/ .