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arXiv · 1705.00368

How to Read Many-Objective Solution Sets in Parallel Coordinates

Miqing Li·Liangli Zhen·Xin Yao
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Abstract

Rapid development of evolutionary algorithms in handling many-objective optimization problems requires viable methods of visualizing a high-dimensional solution set. Parallel coordinates which scale well to high-dimensional data are such a method, and have been frequently used in evolutionary many-objective optimization. However, the parallel coordinates plot is not as straightforward as the classic scatter plot to present the information contained in a solution set. In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives. We hope that these observations could provide some guidelines as to the proper use of parallel coordinates in evolutionary many-objective optimization.

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Miqing Li, Liangli Zhen, Xin Yao. 2017-04-30. How to Read Many-Objective Solution Sets in Parallel Coordinates. https://arxiv.org/abs/1705.00368

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