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

Efficient configurational-bias Monte-Carlo simulations of chain molecules with `swarms' of trial configurations