Externalising abstract mathematical models
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
HyperSlice: visualization of scalar functions of many variables
VIS '93 Proceedings of the 4th conference on Visualization '93
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Visualization of Pareto-Sets in Evolutionary Multi-Objective Optimization
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Information Sciences: an International Journal
Visualization and data mining of Pareto solutions using self-organizing map
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Heatmap visualization of population based multi objective algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
MOGA-II for an automotive cooling duct optimization on distributed resources
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
An approach to visualizing the 3D empirical attainment function
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Visualization and exploration of optimal variants in product line engineering
Proceedings of the 17th International Software Product Line Conference
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In ideal multiobjective optimization, the result produced by an optimizer is a set of nondominated solutions approximating the Pareto optimal front. Visualization of this approximation set can help assess its quality as well as present various features of the problem. Most often, scatter plots are used to visualize 2D and 3D approximation sets, while no scatter plot equivalent exists for visualization in higher dimensions. This paper presents a method for visualizing 4D approximation sets which performs dimension reduction using prosections (projections of a section). The method yields a prosection matrix---a matrix of intuitive 3D scatter plots that well reproduce the shape, range and distribution of vectors in the observed approximation set. The performance of visualization with prosections is analyzed theoretically and demonstrated on two examples with approximation sets of state-of-the-art test optimization problems.