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 behaviour 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 profit profiles influence the runtime behavior of both algorithms in the chance-constrained setting.
|Original language||English (US)|
|Title of host publication||GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|State||Published - Jun 26 2021|
|Event||2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France|
Duration: Jul 10 2021 → Jul 14 2021
|Name||GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference|
|Conference||2021 Genetic and Evolutionary Computation Conference, GECCO 2021|
|Period||7/10/21 → 7/14/21|
Bibliographical noteFunding Information:
This research has been supported by the SA Government through the PRIF RCP Industry Consortium.
© 2021 ACM.
- Chance-constrained knapsack problem
- Evolutionary algorithms
- Run-time analysis