<ul><li>The challenge of noisy multi-objective optimization lies in the constant trade-off between exploring new decision points and improving the precision of known points through resampling.</li><li>This paper proposes a resampling decision function that incorporates the stochastic nature of the optimization problem by using bootstrapping and the probability of dominance.</li><li>The approach utilizes bootstrap estimates of the means to achieve distribution-free estimation of the probability of dominance.</li><li>The resampling approach is demonstrated to be efficient by applying it in the NSGA-II algorithm with a sequential resampling procedure under multiple noise variations.</li></ul>

Adaptive Resampling with Bootstrap for Noisy Multi-Objective Optimization Problems

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