Massively Parallel Evolutionary Markov Chain Monte Carlo for Sampling Complicated Multimodal State Spaces

Wendy Cho, University of Illinois at Urbana-Champaign

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We propose to 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 as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. 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. We aim to build on our previous work by applying our algorithm specifically to substantive issues in the political redistricting arena.