Fitness landscapes were proposed in 1932 as an abstract notion for understanding
biological evolution and were later used to explain evolutionary algorithm behaviour. The …

The herein proposed Python package pflacco provides a set of numerical features to
characterize single-objective continuous and constrained optimization problems. Thereby …

Oftentimes the characteristics of real-world engineering optimization problems are not well
understood. In this paper, we introduce an approach for characterizing highly nonlinear and …

Facilitated by the recent advances of Machine Learning (ML), the automated design of
optimization heuristics is currently shaking up evolutionary computation (EC). Where the …

In numerical optimization, the characterization of optimization problems and their properties
has been a long-standing issue. Overcoming it is a crucial prerequisite for many optimization …

Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a
comprehensive and large set of numerical features characterizing problem instances. Those …

Black-box optimization is a very active area of research, with many new algorithms being
developed every year. This variety is needed, on the one hand, since different algorithms …

Abstract The Black Box Optimization Benchmarking (BBOB) set provides a diverse problem
set for continuous optimization benchmarking. At its core lie 24 functions, which are …

The application of machine learning (ML) models to the analysis of optimization algorithms
requires the representation of optimization problems using numerical features. These …

The choice of which objective functions, or benchmark problems, should be used to test an
optimization algorithm is a crucial part of the algorithm selection framework. Benchmark …