  Several computer vision and artificial intelligence projects are nowadays
exploiting the manifold data distribution using, e.g., the diffusion process.
This approach has produced dramatic improvements on the final performance
thanks to the application of such algorithms to the kNN graph. Unfortunately,
this recent technique needs a manual configuration of several parameters, thus
it is not straightforward to find the best configuration for each dataset.
Moreover, the brute-force approach is computationally very demanding when used
to optimally set the parameters of the diffusion approach. We propose to use
genetic algorithms to find the optimal setting of all the diffusion parameters
with respect to retrieval performance for each different dataset. Our approach
is faster than others used as references (brute-force, random-search and PSO).
A comparison with these methods has been made on three public image datasets:
Oxford5k, Paris6k and Oxford105k.
