Paper Info
Title
Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights
Abstract
ABSTRACTAddressing 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.
Year
DOI
Venue
2021
10.1145/3449639.3459381
Genetic and Evolutionary Computation Conference
Keywords
DocType
Citations 
Evolutionary algorithms, Chance-constrained knapsack problem, Run-time analysis
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yue Xie102.37
Aneta Neumann21412.79
Frank Neumann31727124.28
Andrew Sutton4206.82