Parameter control is aimed at realizing performance gains through a dynamic choice of the
parameters which determine the behavior of the underlying optimization algorithm. In the …

Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations
have become state of the art in algorithm design for solving real-world optimization …

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 …

Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called
Pareto optimisation) as they use a population to store trade-offs between different objectives …

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

In this paper an alternative method to achieve distance based formation is presented. The
method uses Genetic Algorithms to find a suitable solution based on angle and distance …

Drift Analysis Page 1 Chapter 2 Drift Analysis Johannes Lengler Abstract Drift analysis is one
of the major tools for analysing evolutionary algorithms and nature-inspired search heuristics …

We propose a new way to self-adjust the mutation rate in population-based evolutionary
algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate …

We propose and analyze a self-adaptive version of the (1, λ) evolutionary algorithm in which
the current mutation rate is part of the individual and thus also subject to mutation. A rigorous …

Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is
mostly concerned with expected runtimes, occasionally augmented by tail bounds. In this …