During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations
have become state of the art in algorithm design for solving real-world optimization …

Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …

It has long been observed that for practically any computational problem that has been
intensely studied, different instances are best solved using different algorithms. This is …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

As revealed by the no free lunch theorem, no single algorithm can outperform any others on
all classes of optimization problems. To tackle this issue, methods for recommending an …

We expose and contrast the impact of landscape characteristics on the performance of
search heuristics for black-box multiobjective combinatorial optimization problems. A sound …

Exploratory landscape analysis (ELA) supports supervised learning approaches for
automated algorithm selection and configuration by providing sets of features that quantify …

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 …