Evolutionary algorithms for the chance-constrained knapsack problem
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(Accepted version)
Date
2019
Authors
Xie, Y.
Harper, O.
Assimi, H.
Neumann, A.
Neumann, F.
Editors
Auger, A.
Stützle, T.
Stützle, T.
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Conference paper
Citation
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, July 13-17, 2019, 2019 / Auger, A., Stützle, T. (ed./s), vol.abs/1902.04767, pp.338-346
Statement of Responsibility
Xue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann, Frank Neumann
Conference Name
GECCO '19: Genetic and Evolutionary Computation Conference (13 Jul 2019 - 17 Jul 2019 : Prague Czech Republic)
Abstract
Evolutionary algorithms have been widely used for a range of stochastic optimization problems. In most studies, the goal is to optimize the expected quality of the solution. Motivated by real-world problems where constraint violations have extremely disruptive effects, we consider a variant of the knapsack problem where the profit is maximized under the constraint that the knapsack capacity bound is violated with a small probability of at most α. This problem is known as chance-constrained knapsack problem and chance-constrained optimization problems have so far gained little attention in the evolutionary computation literature. We show how to use popular deviation inequalities such as Chebyshev's inequality and Chernoff bounds as part of the solution evaluation when tackling these problems by evolutionary algorithms and compare the effectiveness of our algorithms on a wide range of chance-constrained knapsack instances.
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© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.