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|Title:||Runtime analysis of the (1+1) evolutionary algorithm for the chance-constrained knapsack problem|
|Citation:||FOGA '19: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019, pp.147-153|
|Publisher:||Association for Computing Machinery|
|Conference Name:||Foundations of Genetic Algorithms (FOGA) (27 Aug 2019 - 29 Aug 2019 : Potsdam, Germany)|
|Frank Neumann, Andrew M. Sutton|
|Abstract:||The 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.|
|Keywords:||Knapsack problem; chance-constrained optimization; evolutionary algorithms|
|Rights:||© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.|
|Appears in Collections:||Aurora harvest 8|
Computer Science publications
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