Abstrakt
The paper presents a novel application of artificial neural networks (ANNs) in the context of surrogate models for black-box opti- mization, i.e. optimization of objective functions that are accessed through empirical evaluation. For active learning of surrogate models, a very im- portant role plays learning of multidimensional normal distributions, for which Gaussian processes (GPs) have been traditionally used.
On the other hand, the research reported in this paper evaluated the applicabil- ity of two ANN-based methods to this end: combining GPs with ANNs and learning normal distributions with evidential ANNs. After methods sketch, the paper brings their comparison on a large collection of data from surrogate-assisted black-box optimization.
It shows that combining GPs using linear covariance functions with ANNs yields lower errors than the investigated methods of evidential learning.