MOPBnB(so) approximates the Pareto-optimal set under noisy objectives using one observation per point and neighbor-averaged estimates, with claimed asymptotic convergence and far lower cost than replication-based methods.
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Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
MOPBnB(so) approximates the Pareto-optimal set under noisy objectives using one observation per point and neighbor-averaged estimates, with claimed asymptotic convergence and far lower cost than replication-based methods.