System identification using evolutionary Markov chain Monte Carlo

System identi®cation involves determination of the functional structure of a target system that underlies the observed data. In this paper, we present a probabilistic evolutionary method that optimizes system architectures for the iden-ti®cation of unknown target systems. The method is distinguished from existing evolutionary algorithms (EAs) in that the individuals are generated from a probability distribution as in Markov chain Monte Carlo (MCMC). It is also distinguished from conventional MCMC methods in that the search is population-based as in standard evolutionary algorithms. The e€ectiveness of this hybrid of evolutionary computation and MCMC is tested on a practical problem, i.e., evolving neural net architectures for the identi®cation of nonlinear dynamic systems. Experimental evidence supports that evolutionary MCMC (or eMCMC) exploits the eciency of simple evolutionary algorithms while maintaining the robustness of MCMC methods and outperforms either approach used alone.