Feature-based offline algorithm selection has shown its effectiveness in a
wide range of optimization problems, including the black-box optimization
problem. An algorithm selection system selects the most promising optimizer
from an algorithm portfolio, which is a set of pre-defined optimizers. Thus,
algorithm selection requires a well-constructed algorithm portfolio consisting
of efficient optimizers complementary to each other. Although construction
methods for the fixed-target setting have been well studied, those for the
fixed-budget setting have received less attention. Here, the fixed-budget
setting is generally used for computationally expensive optimization, where a
budget of function evaluations is small. In this context, first, this paper
points out some undesirable properties of experimental setups in previous
studies. Then, this paper argues the importance of considering the number of
function evaluations used in the sampling phase when constructing algorithm
portfolios, whereas the previous studies ignored that. The results show that
algorithm portfolios constructed by our approach perform significantly better
than those by the previous approach.

离线算法选择，算法组合，固定预算设置，函数评估，优化问题。

在固定预算设置下构建算法选择中的算法组合对计算密集型黑箱优化问题的解决

On Constructing Algorithm Portfolios in Algorithm Selection for  Computationally Expensive Black-box Optimization in the Fixed-budget Setting

Algorithm portfolio and selection approaches have achieved remarkable
improvements over single solvers. However, the implementation of such systems
is often highly customised and specific to the problem domain. This makes it
difficult for researchers to explore different techniques for their specific
problems. We present LLAMA, a modular and extensible toolkit implemented as an
R package that facilitates the exploration of a range of different portfolio
techniques on any problem domain. It implements the algorithm selection
approaches most commonly used in the literature and leverages the extensive
library of machine learning algorithms and techniques in R. We describe the
current capabilities and limitations of the toolkit and illustrate its usage on
a set of example SAT problems.

LLAMA 是一种模块化和可扩展的工具包，用于探索各种不同的算法组合方法，以解决问题域中的任何问题。该工具包实现了文献中最常用的算法选择方法，并利用 R 语言中的大量机器学习算法和技术。