The chance-constrained knapsack problem is a variant of the classical knapsack problem
where each item has a weight distribution instead of a deterministic weight. The objective is …

Evolutionary algorithms have been widely used for a range of stochastic optimization
problems. In most studies, the goal is to optimize the expected quality of the solution …

Evolutionary algorithms, being problem-independent and randomized heuristics, are
generally believed to be robust to dynamic changes and noisy access to the problem …

Evolutionary algorithms are bio-inspired algorithms that can easily adapt to changing
environments. In this paper, we study single-and multi-objective baseline evolutionary …

Many real-world optimization problems occur in environments that change dynamically or
involve stochastic components. Evolutionary algorithms and other bio-inspired algorithms …

Rigorous runtime analysis is a major approach towards understanding evolutionary
computing techniques, and in this area linear pseudo-Boolean objective functions play a …

We contribute to the theoretical understanding of randomized search heuristics for dynamic
problems. We consider the classical graph coloring problem and investigate the dynamic …

When a problem instance is perturbed by a small modification, one would hope to find a
good solution for the new instance by building on a known good solution for the previous …

In this paper, we perform theoretical analyses on the behaviour of an evolutionary algorithm
and a randomised search algorithm for the dynamic vertex cover problem based on its dual …

Randomized search heuristics such as evolutionary algorithms are frequently applied to
dynamic combinatorial optimization problems. Within this paper, we present a dynamic …