Population diversity is crucial in evolutionary algorithms to enable global exploration and to
avoid poor performance due to premature convergence. This chapter reviews runtime …

For genetic algorithms (GAs) using a bit-string representation of length n, the general
recommendation is to take 1/n as mutation rate. In this work, we discuss whether this is …

Very recently, the first mathematical runtime analyses of the multiobjective evolutionary
optimizer nondominated sorting genetic algorithm II (NSGA-II) have been conducted. We …

Population diversity is essential for avoiding premature convergence in genetic algorithms
(GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in …

Explaining to what extent the real power of genetic algorithms (GAs) lies in the ability of
crossover to recombine individuals into higher quality solutions is an important problem in …

Very recently, the first mathematical runtime analyses for the NSGA-II, the most common
multi-objective evolutionary algorithm, have been conducted. Continuing this research …

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 …

One hope of using non-elitism in evolutionary computation is that it aids leaving local
optima. We perform a rigorous runtime analysis of a basic non-elitist evolutionary algorithm …

This study presented a new multi-species binary coded algorithm, Mendelian evolutionary
theory optimization (METO), inspired by the plant genetics. This framework mainly consists …

Most evolutionary algorithms have multiple parameters and their values drastically affect the
performance. Due to the often complicated interplay of the parameters, setting these values …