Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Some Methods for Nonlinear Multi-objective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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When evaluating the quality of non---dominated sets, two families of quality indicators are frequently used: unary quality indicators (UQI) and binary quality indicators (BQI). For several years, UQIs have been considered inferior to BQIs. As a result, the use of UQIs has been discouraged, even when in practice they are easier to use. In this work, we study the reasons why UQIs are considered inferior. We make a detailed analysis of the correctness of these reasons and the implicit assumptions in which they are based. The conclusion is that, contrary to what is widely believed, unary quality indicators are not inferior to binary ones.