6 research outputs found
EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
Surrogate algorithms such as Bayesian optimisation are especially designed
for black-box optimisation problems with expensive objectives, such as
hyperparameter tuning or simulation-based optimisation. In the literature,
these algorithms are usually evaluated with synthetic benchmarks which are well
established but have no expensive objective, and only on one or two real-life
applications which vary wildly between papers. There is a clear lack of
standardisation when it comes to benchmarking surrogate algorithms on
real-life, expensive, black-box objective functions. This makes it very
difficult to draw conclusions on the effect of algorithmic contributions. A new
benchmark library, EXPObench, provides first steps towards such a
standardisation. The library is used to provide an extensive comparison of six
different surrogate algorithms on four expensive optimisation problems from
different real-life applications. This has led to new insights regarding the
relative importance of exploration, the evaluation time of the objective, and
the used model. A further contribution is that we make the algorithms and
benchmark problem instances publicly available, contributing to more uniform
analysis of surrogate algorithms. Most importantly, we include the performance
of the six algorithms on all evaluated problem instances. This results in a
unique new dataset that lowers the bar for researching new methods as the
number of expensive evaluations required for comparison is significantly
reduced.Comment: 13 page
Black-box combinatorial optimization using models with integer-valued minima
When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables.AlgorithmicsCyber Securit
Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning
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