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

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

Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms
can provably outperform static settings in evolutionary algorithms for binary search spaces …

Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …

REST web services are widely popular in industry, and search techniques have been
successfully used to automatically generate system-level test cases for those systems. In this …

The most commonly used statistics in Evolutionary Computation (EC) are of the Wilcoxon-
Mann-Whitney-test type, in its either paired or non-paired version. However, using such …

Finding the best configuration of algorithms' hyperparameters for a given optimization
problem is an important task in evolutionary computation. We compare in this work the …

Although deep learning has made remarkable progress in time series forecasting, enormous
hyperparameters consume a lot of effort to tune. Moreover, to further build the forecasting …

It seems very intuitive that for the maximization of the OneMax problem [MATHS HERE] the
best that an elitist unary unbiased search algorithm can do is to store a best so far solution …

This paper proposes a genetic algorithm (GA) combined with ray tracer to generate a cell-
free topology of massive MIMO (mMIMO) for the optimal focusing performance serving …