We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling from large, idiosyncratic, and multi-modal state spaces. Our algorithm combines the advantages of evolutionary algorithms (EAs) as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. We encompass these two algorithms within the framework of a Metropolis-Try Markov Chain with a generalized Metropolis-Hastingratio. We harness the computational power of massively parallel architecture by integrating a parallel EA framework that guides Markov chains running in parallel. Our algorithm has applications in many different fields of science.
