Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
On the computation of the empirical attainment function
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Visualizing 4D approximation sets of multiobjective optimizers with prosections
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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When analyzing the performance of a bi-objective optimization algorithm, the empirical attainment function (EAF) is often used to visualize the attained parts of the objective space. Similarly, when comparing two algorithms, the differences in EAF values can be used to show the parts of the objective space in which the first algorithm outperforms the second one, and vice versa. This paper proposes to visualize the EAF values and differences also when assessing algorithms that optimize three criteria. This can be achieved by cutting through the 3D EAFs using multiple cutting planes and presenting the resulting intersections in 2D. The approach is described in detail and demonstrated on two artificial Pareto front approximations.