NSGA-II: Implementation and Performance Metrics Extraction for CPU and GPU

Abstract

Multi-objective Optimization Evolutionary Algorithms are widely employed for solving different real-world optimization problems. Usually their runs involve a considerable amount of time because of the need to evaluate many functions. This particularity makes them good candidates of parallelization. In this work we investigate the benefits of the GPU implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) versus its CPU implementation in terms of the execution time.