TY - GEN
T1 - Runtime analysis of the (1+1) evolutionary algorithm for the chance-constrained knapsack problem
AU - Neumann, Frank
AU - Sutton, Andrew M.
PY - 2019/8/27
Y1 - 2019/8/27
N2 - 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.
AB - 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.
KW - Chance-constrained optimization
KW - Evolutionary algorithms
KW - Knapsack problem
UR - http://www.scopus.com/inward/record.url?scp=85076409939&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076409939&partnerID=8YFLogxK
U2 - 10.1145/3299904.3340315
DO - 10.1145/3299904.3340315
M3 - Conference contribution
AN - SCOPUS:85076409939
T3 - FOGA 2019 - Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
SP - 147
EP - 153
BT - FOGA 2019 - Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
PB - Association for Computing Machinery, Inc
T2 - 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2019
Y2 - 27 August 2019 through 29 August 2019
ER -