Algorithm selection, a critical process of automated machine learning, aims to identify the
most suitable algorithm for solving a specific problem prior to execution. Mainstream …

The problem of selecting an algorithm that appears most suitable for a specific instance of
an algorithmic problem class, such as the Boolean satisfiability problem, is called instance …

Selecting a well-performing algorithm for a given task or dataset can be time-consuming and
tedious, but is crucial for the successful day-to-day business of develo** new AI & ML …

Hyperparameter optimization (HPO) methods can determine well-performing
hyperparameter configurations efficiently but often lack insights and transparency. We …

Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of
candidate algorithms most suitable for a specific instance of an algorithmic problem class …

In the algorithm selection research, the discussion surrounding algorithm features has been
significantly overshadowed by the emphasis on problem features. Although a few empirical …

It is well known that different algorithms perform differently well on an instance of an
algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic …

In online algorithm selection (OAS), instances of an algorithmic problem class are presented
to an agent one after another, and the agent has to quickly select a presumably best …

Algorithm selection refers to the task of automatically selecting the most suitable algorithm
for solving an instance of a computational problem from a set of candidate algorithms. Here …

Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm
from a fixed set of candidates most suitable for a specific instance of an algorithmic problem …