Black-box Mixed-Variable Optimisation using a Surrogate Model that Satisfies Integer Constraints

A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration. Surrogate-based algorithms are very suitable for this type of problem, but most existing techniques are designed with only continuous or only discrete variables in mind. Mixed-Variable ReLU-based Surrogate Modelling (MVRSM) is a surrogate-based algorithm that uses a linear combination of rectified linear units, defined in such a way that (local) optima satisfy the integer constraints. This method outperforms the state of the art on several synthetic benchmarks with up to 238 continuous and integer variables, and achieves competitive performance on two real-life benchmarks: XGBoost hyperparameter tuning and Electrostatic Precipitator optimisation.Comment: Ann Math Artif Intell (2020

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

Knocking out ACR2 does not affect arsenic redox status in Arabidopsis thaliana: implications for As detoxification and accumulation in plants

Peer reviewedPublisher PD

Raw data of the EXPensive Optimization benchmark library (EXPObench)

These are the raw data of the results of several surrogate-based optimization algorithms, applied to four different real-life expensive optimization problems. Included are the computation time used by the algorithm, time spent on evaluating the expensive objective function, and the values of the decision variables and the objective at every iteration and at the optimum.A file to plot the results is also included, and the plots itself as well.</div

Raw data of the EXPensive Optimization benchmark library (EXPObench)

These are the raw data of the results of several surrogate-based optimization algorithms, applied to four different real-life expensive optimization problems. Included are the computation time used by the algorithm, time spent on evaluating the expensive objective function, and the values of the decision variables and the objective at every iteration and at the optimum. A file to plot the results is also included, and the plots itself as well

Raw data of the EXPensive Optimization benchmark library (EXPObench)

These are the raw data of the results of several surrogate-based optimization algorithms, applied to four different real-life expensive optimization problems. Included are the computation time used by the algorithm, time spent on evaluating the expensive objective function, and the values of the decision variables and the objective at every iteration and at the optimum.A file to plot the results is also included, and the plots itself as well.</div

Effect of <i>AtACR2</i> overexpression on the percentage of As(III) in <i>Arabidopsis thaliana</i>.

<p>Plants were exposed to 5 µM As(V) for 1 day or 1 week.</p

Arsenate (As(V)) uptake and arsenite (As(III)) efflux in wild-type and <i>AtACR2</i> mutants of <i>Arabidopsis thaliana</i>.

<p>Plants were exposed to 5 µM As(V) in a phosphate-free nutrient solution for 6 h.</p