A new Bayesian optimization framework with hierarchical kernels and hierarchical sampling solves a jet engine architecture problem in about 300 evaluations, matching NSGA-II results that previously required 3250.
In: International 9https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest ind from stats.html 45 Conference on Advanced Information Systems Engineering (2022)
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System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems
A new Bayesian optimization framework with hierarchical kernels and hierarchical sampling solves a jet engine architecture problem in about 300 evaluations, matching NSGA-II results that previously required 3250.