论文标题
使用满足整数约束的替代模型的黑盒混合变量优化
Black-box Mixed-Variable Optimisation using a Surrogate Model that Satisfies Integer Constraints
论文作者
论文摘要
在工程和计算机科学中，一个具有挑战性的问题是最大程度地减少我们没有数学公式的功能，这是昂贵的评估，并且包含连续和整数变量，例如在自动算法配置中。基于替代物的算法非常适合这种类型的问题，但是大多数现有技术仅考虑连续或离散变量。基于混合变量的替代替代建模（MVRSM）是一种基于替代物的算法，它使用构造线性单元的线性组合，以这样的方式定义，即（局部）Optima满足整数约束。该方法的表现优于几个合成基准，最多238个连续和整数变量，并在两个现实生活基准上实现竞争性能：XGBoost超参数调整和静电降解器优化。
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.
