Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature. (arXiv:2206.07834v1 [cs.LG])

Web: http://arxiv.org/abs/2206.07834

Many methods for performing multi-objective optimisation of computationally
expensive problems have been proposed recently. Typically, a probabilistic
surrogate for each objective is constructed from an initial dataset. The
surrogates can then be used to produce predictive densities in the objective
space for any solution. Using the predictive densities, we can compute the
expected hypervolume improvement (EHVI) due to a solution. Maximising the EHVI,
we can locate the most promising solution that may be expensively evaluated
next. There are closed-form expressions …

approximation arxiv improvement lg