Optimizing real-world problems often involves decision-making under uncertainty due to the
presence of unknown or uncontrollable variables. Chance-constraints allow to model the …

Evolutionary algorithms are particularly effective for optimisation problems with dynamic and
stochastic components. We propose multi-objective evolutionary approaches for the …

Real-world optimization problems often involve stochastic and dynamic components.
Evolutionary algorithms are particularly effective in these scenarios, as they can easily adapt …

Generating diverse populations of high-quality solutions has gained interest as a promising
extension to the traditional optimization tasks. This work contributes to this line of research …

The travelling thief problem (TTP) is a multi-component combinatorial optimization problem
that has gained significant attention in the evolutionary computation and heuristic search …

In recent years, computing diverse sets of high quality solutions for an optimization problem
has become an important topic. The goal of computing diverse sets of high quality solutions …

Chance-constrained problems involve stochastic components in the constraints which can
be violated with a small probability. We investigate the impact of different types of chance …

Many real-world optimization problems can be stated in terms of submodular functions.
Furthermore, these real-world problems often involve uncertainties which may lead to the …

Recently surrogate functions based on the tail inequalities were developed to evaluate the
chance constraints in the context of evolutionary computation and several Pareto …

In real-world optimisation, it is common to face several sub-problems interacting and forming
the main problem. There is an inter-dependency between the sub-problems, making it …