@inproceedings{96695554b08e4c7d9000b82d67ea2456,
title = "Adjusting parallel coordinates for investigating multi-objective search",
abstract = "Visualizing a high-dimensional solution set over the evolution process is a viable way to investigate the search behavior of evolutionary multi-objective optimization. The parallel coordinates plot which scales well to the data dimensionality is frequently used to observe solution sets in multi-objective optimization. However, the solution sets in parallel coordinates are typically presented by the natural order of the optimized objectives, with rare information of the relation between these objectives and also the Pareto dominance relation between solutions. In this paper, we attempt to adjust parallel coordinates to incorporate this information. Systematic experiments have shown the effectiveness of the proposed method.",
author = "Liangli Zhen and Miqing Li and Ran Cheng and Dezhong Peng and Xin Yao",
year = "2017",
month = oct,
day = "14",
doi = "10.1007/978-3-319-68759-9_19",
language = "English",
isbn = "9783319687582",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "224--235",
editor = "Xiaodong Li and Mengjie Zhang and Qingfu Zhang and Martin Middendorf and Tan, {Kay Chen} and Ying Tan and Yaochu Jin and Yuhui Shi and Ke Tang",
booktitle = "Simulated Evolution and Learning",
note = "11th International Conference on Simulated Evolution and Learning, SEAL 2017 ; Conference date: 10-11-2017 Through 13-11-2017",
}