  Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have
become very popular in signal processing over the last years. In this work, we
introduce a novel MCMC scheme where parallel MCMC chains interact, adapting
cooperatively the parameters of their proposal functions. Furthermore, the
novel algorithm distributes the computational effort adaptively, rewarding the
chains which are providing better performance and, possibly even stopping other
ones. These extinct chains can be reactivated if the algorithm considers
necessary. Numerical simulations shows the benefits of the novel scheme.
