How can we chose to place $k$ points in $[0,1]^d$, such that the minimum Euclidian distance between any two points is maximized?
Is there a more common term for these combinatorial designs than unit hypercube encodings?
Example: For $d=2$ and $k=3$ we could have the points (0.23,0.33), (0.24,0.37), and (0.94, 0.81). The first two points are closest, and this design is far from optimal.
One could also think of this as a Voronoi diagram in the unit hypercube where the minimum distance between any two centroids is maximized.
Here is a the discrete analogue where we force ourselves to use only the value zero or one in each dimension, and Hamming instead of Euclidian distance: http://www.win.tue.nl/~aeb/codes/binary-1.html
Also see, "Sphere Packings, Lattices, and Groups", http://www.springer.com/us/book/9780387985855, which covers dense packings in $R^{n}$, but not packings of points into the unit hypercube from what I can see. In our case the hyperspheres would only be required to have their center point in the unit cube.
SigGraph paper shows success in 2D with uniform sampling then walking each point towards the farthest, http://dl.acm.org/citation.cfm?id=2018345