Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives
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(Published version)
Date
2020
Authors
Assimi, H.
Harper, O.
Xie, Y.
Neumann, A.
Neumann, F.
Editors
Giacomo, G.D.
Catalá, A.
Dilkina, B.
Milano, M.
Barro, S.
Bugarín, A.
Lang, J.
Catalá, A.
Dilkina, B.
Milano, M.
Barro, S.
Bugarín, A.
Lang, J.
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Conference paper
Citation
International Journal of Computer Research, 2020 / Giacomo, G.D., Catalá, A., Dilkina, B., Milano, M., Barro, S., Bugarín, A., Lang, J. (ed./s), vol.325, pp.307-314
Statement of Responsibility
Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann and Frank Neumann
Conference Name
24th European Conference on Artificial Intelligence (ECAI) (29 Aug 2020 - 8 Sep 2020 : Santiago de Compostela, Spain)
Abstract
Real-world combinatorial optimization problems are often stochastic and dynamic. Therefore, it is essential to make optimal and reliable decisions with a holistic approach. In this paper, we consider the dynamic chance-constrained knapsack problem where the weight of each item is stochastic, the capacity constraint changes dynamically over time, and the objective is to maximize the total profit subject to the probability that total weight exceeds the capacity. We make use of prominent tail inequalities such as Chebyshev’s inequality, and Chernoff bound to approximate the probabilistic constraint. Our key contribution is to introduce an additional objective which estimates the minimal capacity bound for a given stochastic solution that still meets the chance constraint. This objective helps to cater for dynamic changes to the stochastic problem. We apply single- and multi-objective evolutionary algorithms to the problem and show how bi-objective optimization can help to deal with dynamic chance-constrained problems.
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© 2020 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).