This chapter collects several probabilistic tools that have proven to be useful in the analysis
of randomized search heuristics. This includes classic material such as the Markov …

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 …

Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

Analyzing Evolutionary Algorithms: The Computer Science Perspective | SpringerLink Skip to
main content Advertisement SpringerLink Log in Menu Find a journal Publish with us Search …

The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent
algorithms to solve multi-objective optimization problems. Recently, the first mathematical …

The analysis of randomized search heuristics on classes of functions is fundamental to the
understanding of the underlying stochastic process and the development of suitable proof …

The NSGA-II is one of the most prominent algorithms to solve multi-objective optimization
problems. Despite numerous successful applications, several studies have shown that the …

Abstract The (1+(λ, λ))(1+(λ, λ)) genetic algorithm proposed in Doerr et al.(Theor Comput Sci
567: 87–104, 2015) is one of the few examples for which a super-constant speed-up of the …

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

A core operator of evolutionary algorithms (EAs) is the mutation. Recently, much attention
has been devoted to the study of mutation operators with dynamic and non-uniform mutation …