This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …

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 …

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 …

Recently a mechanism called stagnation detection was proposed that automatically adjusts
the mutation rate of evolutionary algorithms when they encounter local optima. The so-called …

Recent theoretical studies have shown that self-adjusting mechanisms can provably
outperform the best static parameters in evolutionary algorithms on discrete problems …

In the discrete domain, self-adjusting parameters of evolutionary algorithms (EAs) has
emerged as a fruitful research area with many runtime analyses showing that self-adjusting …

A key challenge to make effective use of evolutionary algorithms (EAs) is to choose
appropriate settings for their parameters. However, the appropriate parameter setting …

Abstract We study the (1, λ)-EA with mutation rate c/n for c≤ 1, where the population size is
adaptively controlled with the (1: s+ 1)-success rule. Recently, Hevia Fajardo and Sudholt …

Dynamic workflow scheduling (DWS) is a challenging and important optimization problem in
cloud computing, aiming to execute multiple heterogeneous workflows on dynamically …