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Analysis of Evolutionary Diversity Optimization for Permutation Problems
2022
ACM Transactions on Evolutionary Learning and Optimization
Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research with an investigation on evolutionary diversity optimization for three of the most well-studied permutation problems, namely the Traveling Salesperson Problem (TSP), both symmetric and asymmetric variants, and Quadratic Assignment Problem (QAP). It includes an analysis of the worst-case performance of a simple
doi:10.1145/3561974
fatcat:irpuqt7ix5he7plwcry6qjsili