  We present a novel Ensemble Monte Carlo Growth method to sample the
equilibrium thermodynamic properties of random chains. The method is based on
the multicanonical technique of computing the density of states in the energy
space. Such a quantity is temperature independent, and therefore microcanonical
and canonical thermodynamic quantities, including the free energy, entropy, and
thermal averages, can be obtained by re-weighting with a Boltzmann factor. The
algorithm we present combines two approaches: the first is the Monte Carlo
ensemble growth method, where a "population" of samples in the state space is
considered, as opposed to traditional sampling by long random walks, or
iterative single-chain growth. The second is the flat-histogram Monte Carlo,
similar to the popular Wang-Landau sampling, or to multicanonical chain-growth
sampling. We discuss the performance and relative simplicity of the proposed
algorithm, and we apply it to known test cases.
