The selection of the most appropriate algorithm to solve a given problem
instance, known as algorithm selection, is driven by the potential to
capitalize on the complementary performance of different algorithms across sets
of problem instances. However, determining the optimal algorithm for an unseen
problem instance has been shown to be a challenging task, which has garnered
significant attention from researchers in recent years. In this survey, we
conduct an overview of the key contributions to algorithm selection in the
field of single-objective continuous black-box optimization. We present ongoing
work in representation learning of meta-features for optimization problem
instances, algorithm instances, and their interactions. We also study machine
learning models for automated algorithm selection, configuration, and
performance prediction. Through this analysis, we identify gaps in the state of
the art, based on which we present ideas for further development of
meta-feature representations.

通过对单目标连续黑箱优化领域的算法选择的关键贡献进行概述，我们介绍了在优化问题实例、算法实例及其交互的元特征表示学习方面的正在进行的工作，并研究了用于自动算法选择、配置和性能预测的机器学习模型，通过这些分析，我们发现了现有技术的不足之处，并提出了进一步发展元特征表示的思路。

黑盒单目标连续优化算法自动选择使用的元特征调查

A Survey of Meta-features Used for Automated Selection of Algorithms for  Black-box Single-objective Continuous Optimization

When solving decision and optimisation problems, many competing algorithms
(model and solver choices) have complementary strengths. Typically, there is no
single algorithm that works well for all instances of a problem. Automated
algorithm selection has been shown to work very well for choosing a suitable
algorithm for a given instance. However, the cost of training can be
prohibitively large due to running candidate algorithms on a representative set
of training instances. In this work, we explore reducing this cost by choosing
a subset of the training instances on which to train. We approach this problem
in three ways: using active learning to decide based on prediction uncertainty,
augmenting the algorithm predictors with a timeout predictor, and collecting
training data using a progressively increasing timeout. We evaluate
combinations of these approaches on six datasets from ASLib and present the
reduction in labelling cost achieved by each option.

从 ASLib 中的六个数据集中评估了三种方法：基于预测不确定性的主动学习、使用超时预测器增强算法预测器以及使用逐步增加超时的方式收集训练数据，并展示了每个选项所实现的标记成本的降低。

节约型算法选择

Frugal Algorithm Selection

The performance of automated algorithm selection (AAS) strongly depends on
the portfolio of algorithms to choose from. Selecting the portfolio is a
non-trivial task that requires balancing the trade-off between the higher
flexibility of large portfolios with the increased complexity of the AAS task.
In practice, probably the most common way to choose the algorithms for the
portfolio is a greedy selection of the algorithms that perform well in some
reference tasks of interest.
We set out in this work to investigate alternative, data-driven portfolio
selection techniques. Our proposed method creates algorithm behavior
meta-representations, constructs a graph from a set of algorithms based on
their meta-representation similarity, and applies a graph algorithm to select a
final portfolio of diverse, representative, and non-redundant algorithms. We
evaluate two distinct meta-representation techniques (SHAP and performance2vec)
for selecting complementary portfolios from a total of 324 different variants
of CMA-ES for the task of optimizing the BBOB single-objective problems in
dimensionalities 5 and 30 with different cut-off budgets. We test two types of
portfolios: one related to overall algorithm behavior and the `personalized'
one (related to algorithm behavior per each problem separately). We observe
that the approach built on the performance2vec-based representations favors
small portfolios with negligible error in the AAS task relative to the virtual
best solver from the selected portfolio, whereas the portfolios built from the
SHAP-based representations gain from higher flexibility at the cost of
decreased performance of the AAS. Across most considered scenarios,
personalized portfolios yield comparable or slightly better performance than
the classical greedy approach. They outperform the full portfolio in all
scenarios.

采用基于元表示的数据驱动技术对算法进行组合选择，实现多样性、代表性和非冗余的算法组合，并且相比传统贪婪方法，个性化组合在大多数情况下表现相当或稍好。

PS-AAS: 黑盒优化中自动算法选择的投资组合选取

PS-AAS: Portfolio Selection for Automated Algorithm Selection in  Black-Box Optimization

A key component of automated algorithm selection and configuration, which in
most cases are performed using supervised machine learning (ML) methods is a
good-performing predictive model. The predictive model uses the feature
representation of a set of problem instances as input data and predicts the
algorithm performance achieved on them. Common machine learning models struggle
to make predictions for instances with feature representations not covered by
the training data, resulting in poor generalization to unseen problems. In this
study, we propose a workflow to estimate the generalizability of a predictive
model for algorithm performance, trained on one benchmark suite to another. The
workflow has been tested by training predictive models across benchmark suites
and the results show that generalizability patterns in the landscape feature
space are reflected in the performance space.

本研究提出了一种可以估算算法性能预测模型泛化能力的方法，并通过在基准测试套件之间训练预测模型来测试该方法的可行性，结果表明，特征空间中的泛化模式确实反映在性能空间中。