  We propose a modification, based on the RESTART (repetitive simulation trials
after reaching thresholds) and DPR (dynamics probability redistribution) rare
event simulation algorithms, of the standard diffusion Monte Carlo (DMC)
algorithm. The new algorithm has a lower variance per workload, regardless of
the regime considered. In particular, it makes it feasible to use DMC in
situations where the "na\"ive" generalisation of the standard algorithm would
be impractical, due to an exponential explosion of its variance. We numerically
demonstrate the effectiveness of the new algorithm on a standard rare event
simulation problem (probability of an unlikely transition in a Lennard-Jones
cluster), as well as a high-frequency data assimilation problem.
