KIT | KIT-Bibliothek | Impressum | Datenschutz

Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations

Taubert, Oskar ORCID iD icon 1; Weiel, Marie ORCID iD icon 1; Coquelin, Daniel ORCID iD icon 1; Farshian, Anis 1; Debus, Charlotte 1; Schug, Alexander 1; Streit, Achim ORCID iD icon 1; Götz, Markus ORCID iD icon 1
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, Propulate 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 Propulate to the established optimization tool Optuna. We find that Propulate 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 this https URL: https://github.com/Helmholtz-AI-Energy/propulate


Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2023
Sprache Englisch
Identifikator KITopen-ID: 1000160009
HGF-Programm 46.21.04 (POF IV, LK 01) HAICU
Weitere HGF-Programme 46.21.02 (POF IV, LK 01) Cross-Domain ATMLs and Research Groups
Umfang 18 S.
Vorab online veröffentlicht am 20.01.2023
Schlagwörter Genetic Optimization, AI, Parallelization, Evolutionary Algorithm
Nachgewiesen in arXiv
Dimensions
Relationen in KITopen
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page