Appel, S. Geithner, W. Reimann, S. Sapinski, M. Singh, R. Vilsmeier, D.M. JACoW Publishing Geneva, Switzerland 978-3-95450-200-4 10.18429/JACoW-ICAP2018-SAPAF02 English 15-21 SAPAF02 injection emittance simulation synchrotron space-charge Contribution to a conference proceedings 2019 2019-01 https://doi.org/10.18429/JACoW-ICAP2018-SAPAF02 http://jacow.org/icap2018/papers/sapaf02.pdf The application of machine learning and nature-inspired optimization methods, like for example genetic algorithms (GA) and particle swarm optimization (PSO) can be found in various scientific/technical areas. In recent years, those approaches are finding application in accelerator physics to a greater extent. In this report, nature-inspired optimization as well as the machine learning will be shortly introduced and their application to the accelerator facility at GSI/FAIR will be presented. For the heavy-ion synchrotron SIS18 at GSI, the multi-objective GA/PSO optimization resulted in a significant improvement of multi-turn injection performance and subsequent transmission for intense beams. An automated injection optimization with genetic algorithms at the CRYRING@ESR ion storage ring has been performed. The usage of machine learning for a beam diagnostic application, where reconstruction of space-charge distorted beam profiles from ionization profile monitors is performed, will also be shown. First results and the experience gained will be presented.