The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

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

Benchmarking and performance analysis play an important role in understanding the
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …

Abstract The Space-Free Multi-Row Facility Layout problem (SF-MRFLP) seeks for a non-
overlapping layout of departments (facilities) on a given number of rows satisfying the …

Although being a crucial question for the development of machine learning algorithms, there
is still no consensus on how to compare classifiers over multiple data sets with respect to …

In the field of optimization and machine learning, the statistical assessment of results has
played a key role in conducting algorithmic performance comparisons. Classically, null …

Traditional estimation of distribution algorithms (EDAs) often use Gaussian densities to
optimize continuous functions, such as the estimation of Gaussian network algorithms …

Reproducibility of experiments is a complex task in stochastic methods such as evolutionary
algorithms or metaheuristics in general. Many works from the literature give general …

Numerical comparison serves as a major tool in evaluating the performance of optimization
algorithms, especially nondeterministic algorithms, but existing methods may suffer from a …

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