  Inference algorithms based on evolving interactions between replicated
solutions are introduced and analyzed on a prototypical NP-hard problem - the
capacity of the binary Ising perceptron. The efficiency of the algorithm is
examined numerically against that of the parallel tempering algorithm, showing
improved performance in terms of the results obtained, computing requirements
and simplicity of implementation.
