论文信息 - Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed

Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed

Speeding-up black-box optimization algorithms via learning and using a surrogate model is a heavily studied topic. This paper evaluates two different surrogate models: Gaussian processes and random forests which are interconnected with the state-of-the art optimization algorithm CMA-ES. Results on the BBOB testing set show that considerable amount of fitness evaluations can be saved especially during the initial phase of the algorithm's progress.

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