Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
An experimental investigation of model-based parameter optimisation: SPO and beyond
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Time-bounded sequential parameter optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Sequential model-based optimization for general algorithm configuration
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Parallel algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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We benchmark a sequential model-based optimization procedure, SMAC-BBOB, on the BBOB set of blackbox functions. We demonstrate that with a small budget of 10xD evaluations of D-dimensional functions, SMAC-BBOB in most cases outperforms the state-of-the-art blackbox optimizer CMA-ES. However, CMA-ES benefits more from growing the budget to 100xD, and for larger number of function evaluations SMAC-BBOB also requires increasingly large computational resources for building and using its models.