Perhaps surprisingly, it is possible to predict how long an algorithm will take to run on a
previously unseen input, using machine learning techniques to build a model of the …

Developers of high-performance algorithms for hard computational problems increasingly
take advantage of automated parameter tuning and algorithm configuration tools, and …

Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about
the reproducibility and replicability of such studies have increased in recent times, reflecting …

This open access book provides a wealth of hands-on examples that illustrate how
hyperparameter tuning can be applied in practice and gives deep insights into the working …

This paper proposes an approach for the analysis and comparison of stochastic solvers
based on the statistical distribution of their variables. The observed variables of the …

Most state-of-the-art algorithms for large-scale optimization problems expose free
parameters, giving rise to combinatorial spaces of possible configurations. Typically, these …

In this article, we attempt to understand and to contrast the impact of problem features on the
performance of randomized search heuristics for black-box multiobjective combinatorial …

Research on new optimization algorithms is often funded based on the motivation that such
algorithms might improve the capabilities to deal with real-world and industrially relevant …

Frequentist statistical methods, such as hypothesis testing, are standard practices in studies
that provide benchmark comparisons. Unfortunately, these methods have often been …

In real-world optimization, it is often expensive to evaluate the quality of a candidate
solution. The costs may be due to run-time of a complex computer simulation, time required …