PEAR: a massively parallel evolutionary computation approach for political redistricting optimization and analysis

Keyphrases

• Computational Approach 100%
• Massively Parallel 100%
• Parallel Evolutionary Algorithms 100%
• Political Redistricting 100%
• Evolutionary Algorithms 66%
• Redistricting 66%
• Search Process 33%
• Evolutionary 33%
• Parallelization 33%
• Search Algorithm 33%
• Supreme Court 33%
• NP-hard 33%
• Optimization Problem 33%
• Legal Requirements 33%
• Computing Power 33%
• Fine Levels 33%
• Solution Space 33%
• Spatial Configuration 33%
• High Performance Computing 33%
• Political Science 33%
• Numerical Efficiency 33%
• Massively Parallel Computation 33%
• Spatial Characteristics 33%
• Combinatorial Optimization Problem 33%
• Substantive Knowledge 33%
• Science Data 33%
• Granularity Level 33%
• Geographic Information Science 33%
• Democratic Ideals 33%
• Message Passing Model 33%
• Partisan Gerrymandering 33%

Computer Science

• Computational Approach 100%
• Evolutionary Computation 100%
• Evolutionary Algorithms 100%
• Geographic Information 50%
• High-Performance Computing 50%
• Experimental Result 50%
• Optimization Problem 50%
• Supercomputer 50%
• Parallelization 50%
• Legal Requirement 50%
• Computing Power 50%
• Solution Space 50%
• Parallel Computing 50%
• Message Passing 50%
• Granularity 50%
• Combinatorial Optimization Problem 50%
• Spatial Configuration 50%