This paper reviews efforts to characterise the hardness of optimisation problem instances,
and to develop improved methodologies for empirical testing of the strengths and …

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 …

Abstract Machine-learning approaches to algorithm-selection typically take data describing
an instance as input. Input data can take the form of features derived from the instance …

The choice of input-data used to train algorithm-selection models is recognised as being a
critical part of the model success. Recently, feature-free methods for algorithm-selection that …

Algorithm selection aims to identify the most suitable algorithm for solving a specific problem
before execution, which has become a critical process of the AutoML. Current mainstream …

Per-instance automated algorithm selection (AAS) aims at leveraging the complementarity of
optimization algorithms with respect to different problem types. State-of-the-art AAS methods …

Algorithm-selection (AS) methods are essential in order to obtain the best performance from
a portfolio of solvers over large sets of instances. However, many AS methods rely on an …

Generating new instances via evolutionary methods is commonly used to create new
benchmarking data-sets, with a focus on attempting to cover an instance-space as …

The island model is an effective alternative to implement a standalone, hybrid, or parallel
evolutionary algorithm that has been focused in the last decade. To make this model more …

Constraint modelling languages such as Essence offer a means to describe combinatorial
problems at a high-level, ie, without committing to detailed modelling decisions for a …