  Experimental designs are tools which can dramatically reduce the number of
simulations required by time-consuming computer codes. Because we don't know
the true relation between the response and inputs, designs should allow one to
fit a variety of models and should provide information about all portions of
the experimental region. One strategy for selecting the values of the inputs at
which to observe the response is to choose these values so they are spread
evenly throughout the experimental region, according to "space-filling
designs". In this article, we suggest a new method based on comparing the
empirical distribution of the points in a design to the uniform distribution
with the Kullback-Leibler information. The considered approach consists in
estimating this difference or, reciprocally, the Shannon entropy. The entropy
is estimated by a Monte Carlo method where the density function is replaced by
its kernel density estimator or by using the nearest neighbor distances
