# How to Read Many-Objective Solution Sets in Parallel Coordinates

arXiv: https://arxiv.org/abs/1705.00368 · HTML: https://mlsanity.com/paper/1705.00368/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [CACTUS: Detecting and Resolving Conflicts in Objective Functions](https://mlsanity.com/paper/2103.07805/) (2021)
- [A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization](https://mlsanity.com/paper/2308.05640/) (2023)
- [ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms](https://mlsanity.com/paper/2501.02857/) (2025)
- [Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots](https://mlsanity.com/paper/2507.02905/) (2025)

## Abstract (arXiv)

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.