Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights

Yue Xie, Aneta Neumann, Frank Neumann, Andrew M. Sutton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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 languageEnglish (US)
Title of host publicationGECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages1187-1194
Number of pages8
ISBN (Electronic)9781450383509
DOIs
StatePublished - Jun 26 2021
Event2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
Duration: Jul 10 2021Jul 14 2021

Publication series

NameGECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference

Conference

Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Country/TerritoryFrance
CityVirtual, Online
Period7/10/217/14/21

Bibliographical note

Funding Information:
This research has been supported by the SA Government through the PRIF RCP Industry Consortium.

Publisher Copyright:
© 2021 ACM.

Keywords

  • Chance-constrained knapsack problem
  • Evolutionary algorithms
  • Run-time analysis

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