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• W2963753818 abstract "Diversity preservation plays an important role in the design of multi-objective evolutionary algorithms, but the diversity performance assessment of these algorithms remains challenging. To address this issue, this paper proposes a performance metric and a multi-objective test suite for the diversity assessment of multiobjective evolutionary algorithms. The proposed metric assesses both the evenness and spread of a solution set by projecting it to a lower-dimensional hypercube and calculating the volume of the projected solution set. The proposed test suite contains eight benchmark problems, which pose stiff challenges for existing algorithms to obtain a diverse solution set. Experimental studies demonstrate that the proposed metric can assess the diversity of a solution set more precisely than existing ones, and the proposed test suite can be used to effectively distinguish between algorithms with respect to their diversity performance." @default.
• W2963753818 created "2019-07-30" @default.
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• W2963753818 date "2019-08-01" @default.
• W2963753818 modified "2023-10-10" @default.
• W2963753818 title "Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems [Research Frontier]" @default.
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• W2963753818 doi "https://doi.org/10.1109/mci.2019.2919398" @default.
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