Expected Hypervolume Maximization for Multiobjective Optimization under Uncertainties
Read the original on arXiv Machine Learning →The paper proposes a Bayesian decision framework for multiobjective optimization under uncertainty, focusing on maximizing the expected hypervolume over a finite set of input points. It demonstrates that gradient‑based stochastic optimization can be applied, especially when dominated points are handled carefully, and suggests using Gaussian Processes as differentiable surrogate models when direct gradients are unavailable. Additionally, the authors introduce active learning strategies via acquisition functions to build surrogate models tailored to the multiobjective problem and evaluate these strategies on simple analytical benchmarks.
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