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

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

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 …

Understanding how the time complexity of evolutionary algorithms (EAs) depend on their
parameter settings and characteristics of fitness landscapes is a fundamental problem in …

Runtime analysis has produced many results on the efficiency of simple evolutionary
algorithms like the (1+ 1) EA, and its analogue called GSEMO in evolutionary multiobjective …

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 …

The benefits of using crossover in crossing fitness gaps have been studied extensively in
evolutionary computation. Recent runtime results show that majority-vote crossover is …

It is known that the (1+ 1)-EA with mutation rate c/n optimizes every monotone function
efficiently if c<; 1, and needs exponential time on some monotone functions (HOTTOPIC …

Because of being imprecision and existence of uncertainty in input variables to fuzzy
systems, and also their easy implementation, fuzzy controllers are introduced as one of …

Memetic algorithms integrate local search into an evolutionary algorithm to combine the
advantages of rapid exploitation and global optimisation. We provide a rigorous runtime …