Ryo Tamura, Kei Terayama, Masato Sumita, and Koji Tsuda (2023)
Highlighted by Jan Jensen
Figure 1 from the paper. (c) APS 2023. Reproduced under the CC-BY license.
One of the main challenges in multi-objective optimisation is how to weigh the different objectives to get the desired results. Pareto optimisation can in principle solve this problem, but of you get too many solutions you have to select a subset for testing, which basically involves (manually) weighing the importance of each objective.
This paper proposes a new way to select the potentially most interesting candidates. The idea is basically to identify the most "novel" candidates to maximise the chances of finding "interesting" properties, They do this by identifying points on the Pareto front with the lowest "density of states" for each objective, i.e. points with few examples in property space.
The method is presented as a post hoc selection method, but could also be used as a search criteria to help focus the search on these areas of property spaces.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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