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 …

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

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 …

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

In this article, we build upon previous work on designing informative and efficient
Exploratory Landscape Analysis features for characterizing problems' landscapes and show …

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …

In recent years, many different differential evolution (DE) variants have been proposed to
solve real-world optimization problems. However, the performance of them is largely …

Selecting the most appropriate algorithm to use when attempting to solve a black-box
continuous optimization problem is a challenging task. Such problems typically lack …

Choosing the best-performing optimizer (s) out of a portfolio of optimization algorithms is
usually a difficult and complex task. It gets even worse, if the underlying functions are …

Per-instance algorithm selection seeks to recommend, for a given problem instance and a
given performance criterion, one or several suitable algorithms that are expected to perform …