An interesting idea is to use meta-model (a priori representation of the problem) as a filter to bias the sample produced by metaheuristics. This approach seems especially promising for engineering problem, where computing the objective function is very expensive.

One simple form of meta-model is a probability density function,
approximating the shape of the objective function. This PDF could thus be used
to filter out bad points *before* evaluation.

Why, then, do not directly use EDA to generate the sample ? Because one can imagine that the problem shape is not well known, and that using a complex PDF is impossible (too expensive to compute, for example). Then, using a classical indirect metaheuristic (let say an evolutionary algorithm) should be preferable (computationnaly inexpensive) for the sample generation. If one know a good approximation to use for the distribution of the EDA (not too computationnaly expensive), one can imagine using the best part of the two worlds.

An example could be a problem with real variable : using an EDA with a
multi-variate normal distribution is computationnaly expensive (due to the
estimation of the co-variance, mainly), and using a mixture of gaussian kernels
makes difficult to have an *a priori* on the problem. Thus, why not
using a indirect metaheuristic to handle the sample generation, and use a
meta-model which parameters are estimated from the previous sample, according
to a chosen distribution ?

One more hybridization to try...