  Proposed here is a dynamic Monte-Carlo algorithm that is efficient in
simulating dense systems of long flexible chain molecules. It expands on the
configurational-bias Monte-Carlo method through the simultaneous generation of
a large set of trial configurations. This process is directed by attempting to
terminate unfinished chains with a low statistical weight, and replacing these
chains with clones (enrichments) of stronger chains. The efficiency of the
resulting method is explored by simulating dense polymer brushes. A gain in
efficiency of at least three orders of magnitude is observed with respect to
the configurational-bias approach, and almost one order of magnitude with
respect to recoil-growth Monte-Carlo. Furthermore, the inclusion of `waste
recycling' is observed to be a powerful method for extracting meaningful
statistics from the discarded configurations.
