  Experimental designs are tools which can drastically reduce the number of
simulations required by time-consuming computer codes. One strategy for
selecting the values of the inputs, whose response is to be observed, is to
choose these values so that they are spread evenly throughout the experimental
region, according to ?space filling designs?. In this article, we suggest a new
criterion based on the Kullback-Leibler information for design construction.
The aim is to minimize the difference between the empirical distribution of the
design points and the uniform distribution which is equivalent to maximizing
the Shannon entropy. The entropy is estimated by a Monte Carlo method, where
the density function is replaced with its kernel density estimator or by using
the nearest neighbor distances.
