One-shot optimization for vehicle dynamics control systems
Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 2036–2045
Abstract
Many real-world black-box optimization problems from industry are computationally expensive. Due to the advantage in wall-clock time, fully parallel sampling (one-shot search) is therefore often chosen over iterative search and adaptive sampling approaches. Our contribution shows how using a surrogate model (one-shot optimization with surrogate) can enhance the best solution found within the initial sample, requiring no further problem evaluations.
Authors 4
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BMW (Germany) · BMW Group (Germany)
Affiliation as printed
BMW Group, München, Germany
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Anna V. Kononova Aachen
Affiliation as printed
Leiden University, Leiden, The Netherlands
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BMW (Germany) · BMW Group (Germany)
Affiliation as printed
BMW Group, München, Germany
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Thomas Bäck Aachen
Affiliation as printed
Leiden University, Leiden, The Netherlands
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