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 …

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 …

Automated benchmarking environments aim to support researchers in understanding how
different algorithms perform on different types of optimization problems. Such comparisons …

The design and development of metaheuristic algorithms can be time-consuming and
difficult for a number of reasons including the complexity of the problems being tackled, the …

Evolution strategies are a class of evolutionary algorithms for black-box optimization and
achieve state-of-the-art performance on many benchmarks and real-world applications …

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

Existing studies in black-box optimization suffer from low generalizability, caused by a
typically selective choice of problem instances used for training and testing of different …

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