摘要
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces.To reach target properties efficiently,these platforms are increasingly paired with intelligent experimental design.However,current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints.Here,we devise an Evolution-Guided Bayesian Optimization(EGBO)algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement(qNEHVI)optimizer;this not only solves for the Pareto Front(PF)efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space.
基金
funding from AME Programmatic Funds by the Agency for Science,Technology and Research under Grant No.A1898b0043 and No.A20G9b0135
KH also acknowledges funding from the National Research Foundation(NRF),Singapore under the NRF Fellowship(NRF-NRFF13-2021-0011)
SAK and FMB also acknowledge funding from the 25th NRF CRP programme(NRF-CRP25-2020RS-0002)
QL also acknowledges support from the NRF fellowship(project No.NRF-NRFF13-2021-0005)
the Ministry of Education,Singapore,under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials(I-FIM,project No.EDUNC-33-18-279-V12).
