  Capacity is an important tool in decision-making under risk and uncertainty
and multi-criteria decision-making. When learning a capacity-based model, it is
important to be able to generate uniformly a capacity. Due to the monotonicity
constraints of a capacity, this task reveals to be very difficult. The
classical Random Node Generator (RNG) algorithm is a fast-running speed
capacity generator, however with poor performance. In this paper, we firstly
present an exact algorithm for generating a $n$ elements' general capacity,
usable when $n < 5$. Then, we present an improvement of the classical RNG by
studying the distribution of the value of each element of a capacity.
Furthermore, we divide it into two cases, the first one is the case without any
conditions, and the second one is the case when some elements have been
generated. Experimental results show that the performance of this improved
algorithm is much better than the classical RNG while keeping a very reasonable
computation time.
