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dc.contributor.authorNeumann, F.-
dc.contributor.authorSutton, A.-
dc.identifier.citationFOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019, pp.147-153-
dc.description.abstractThe area of runtime analysis has made important contributions to the theoretical understanding of evolutionary algoirthms for stochastic problems in recent years. Important real-world applications involve chance constraints where the goal is to optimize a function under the condition that constraints are only violated with a small probability. We rigorously analyze the runtime of the (1+1) EA for the chance-constrained knapsack problem. In this setting, the weights are stochastic, and the objective is to maximize a linear profit function while minimizing the probability of a constraint violation in the total weight. We investigate a number of special cases for this problem, paying attention to how the structure of the chance constraint influences the runtime behavior of the (1+1) EA. Our results reveal that small changes to the profit value can result in hard-to-escape local optima.-
dc.description.statementofresponsibilityFrank Neumann, Andrew M. Sutton-
dc.publisherAssociation for Computing Machinery-
dc.rights© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.-
dc.subjectKnapsack problem; chance-constrained optimization; evolutionary algorithms-
dc.titleRuntime analysis of the (1+1) evolutionary algorithm for the chance-constrained knapsack problem-
dc.typeConference paper-
dc.contributor.conferenceFoundations of Genetic Algorithms (FOGA) (27 Aug 2019 - 29 Aug 2019 : Potsdam, Germany)-
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]-
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