Name
Abstract | ||
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In multiobjective optimization, many techniques are used to visualize the results, ranging from traditional general-purpose data visualization techniques to approaches tailored to the specificities of multiobjective optimization. The number of specialized approaches rapidly grows in the recent years. To assist both the users and developers in this field, we propose a taxonomy of methods for visualizing Pareto front approximations. It builds on the nature of the visualized data and the properties of visualization methods rather than on the employed visual representations. It covers the methods for visualizing individual approximation sets resulting from a single algorithm run as well as multiple approximation sets produced in repeated runs. The proposed taxonomy categories are characterized and illustrated with selected examples of visualization methods. We expect that proposed taxonomy will be insightful to the multiobjective optimization community, make the communication among the participants easier and help focus further development of visualization methods.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3205455.3205607 | GECCO |
Keywords | Field | DocType |
multiobjective optimization, visualization methods, taxonomy | Data visualization,Visualization,Computer science,Multi-objective optimization,Ranging,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5618-3 | 2 | 0.35 |
References | Authors | |
30 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bogdan Filipic | 1 | 361 | 26.93 |
| Tea Tusar | 2 | 181 | 19.91 |