Massively Parallel Evolutionary Computation for Empowering Electoral Reform: Quantifying Gerrymandering via Multi-objective Optimization and Statistical Analysis

Wendy Cho
University of Illinois at Urbana-Champaign

Important insights into redistricting can be gained by formulating and analyzing the problem within a large-scale optimization framework. Redistricting is an application of the graph-partitioning problem that is NP-Hard. We design a hybrid metaheuristic as the base search algorithm. Using the Blue Waters supercomputer, we extend our algorithm to the high-performance-computing realm by using MPI to implement an asynchronous inter-process communication framework. We experimentally demonstrate the effectiveness of our algorithm to utilize hundreds of thousands of processors for the redistricting problem. The massive computing power allows us to extract new substantive insights that closely mesh with the framework that the Supreme Court has elucidated for electoral reform.

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