  Addressing a complex real-world optimization problem is a challenging task.
The chance-constrained knapsack problem with correlated uniform weights plays
an important role in the case where dependent stochastic components are
considered. We perform runtime analysis of a randomized search algorithm (RSA)
and a basic evolutionary algorithm (EA) for the chance-constrained knapsack
problem with correlated uniform weights. We prove bounds for both algorithms
for producing a feasible solution. Furthermore, we investigate the behavior of
the algorithms and carry out analyses on two settings: uniform profit value and
the setting in which every group shares an arbitrary profit profile. We provide
insight into the structure of these problems and show how the weight
correlations and the different types of profit profiles influence the runtime
behavior of both algorithms in the chance-constrained setting.
