DynamoRep: Trajectory-Based Population Dynamics for Classification of
  Black-box Optimization Problems
Gjorgjina Cenikj Gašper Petelin Carola Doerr Peter Korošec Tome Eftimov
The application of machine learning (ML) models to the analysis of
optimization algorithms requires the representation of optimization problems
using numerical features. These features can be used as input for ML models
that are trained to select or to configure a suitable algorithm for the problem
at hand. Since in pure black-box optimization information about the problem
instance can only be obtained through function evaluation, a common approach is
to dedicate some function evaluations for feature extraction, e.g., using
random sampling. This approach has two key downsides: (1) It reduces the budget
left for the actual optimization phase, and (2) it neglects valuable
information that could be obtained from a problem-solver interaction.
  In this paper, we propose a feature extraction method that describes the
trajectories of optimization algorithms using simple descriptive statistics. We
evaluate the generated features for the task of classifying problem classes
from the Black Box Optimization Benchmarking (BBOB) suite. We demonstrate that
the proposed DynamoRep features capture enough information to identify the
problem class on which the optimization algorithm is running, achieving a mean
classification accuracy of 95% across all experiments.