Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization?

Motivated by the potential to capitalize on the varied performance of different algorithms across sets of different problem instances, the algorithm selection (AS) task targets the automated identification of a preferred optimization algorithm to solve a particular problem instance Kotthoff [2016], Kerschke et al. [2019]. Conventionally, AS is performed by taking into account the properties of the problem instance, which are typically described in the form of a numerical vector representation, also referred to as problem landscape features. Once a problem instance is represented in a vector form, Machine Learning (ML) models can be used to capture the relation between problem landscape features and algorithm performance, and further identify the best algorithm for a problem instance. In the field of single-objective continuous optimization, the most common choice of problem landscape features used to represent problem instances are the Exploratory Landscape Analysis (ELA) [Mersmann et al., 2011] features.