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We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm forsampling spatial partitions that lie within a large and complex spatial statespace. Our algorithm combines the advantages of evolutionary algorithms (EAs)as optimization heuristics for state space traversal and the theoreticalconvergence properties of Markov Chain Monte Carlo algorithms for sampling fromunknown distributions. Local optimality information that is identified via adirected search by our optimization heuristic is used to adaptively update aMarkov chain in a promising direction within the framework of a Multiple-TryMetropolis Markov Chain model that incorporates a generalizedMetropolis-Hasting ratio. We further expand the reach of our EMCMC algorithm byharnessing the computational power afforded by massively parallel architecturethrough the integration of a parallel EA framework that guides Markov chainsrunning in parallel.

A Parallel Evolutionary Multiple-Try Metropolis Markov Chain Monte Carlo Algorithm for Sampling Spatial Partitions