Blackbox: A procedure for parallel optimization of expensive black-box functions
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Abstract (arXiv)
This note provides a description of a procedure that is designed to efficiently optimize expensive black-box functions. It uses the response surface methodology by incorporating radial basis functions as the response model. A simple method based on a Latin hypercube is used for initial sampling. A modified version of CORS algorithm with space rescaling is used for the subsequent sampling. The procedure is able to scale on multicore processors by performing multiple function evaluations in parallel. The source code of the procedure is written in Python.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2016.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2022Sampling in Dirichlet Process Mixture Models for Clustering Streaming Datacited by 6
- 2023Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMscited by 5
- 2026Security Risks in Machining Process Monitoring: Sequence-to-Sequence Learning for Reconstruction of CNC Axis Positionscited by 0
- 2026Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimizationcited by 0
- 2026Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization4 Sep 2026
- 2026Security Risks in Machining Process Monitoring: Sequence-to-Sequence Learning for Reconstruction of CNC Axis Positions2 Mar 2026
- 2023Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs23 Feb 2023
- 2022Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data27 Feb 2022