Summary of Illuminating the Diversity-fitness Trade-off in Black-box Optimization, by Maria Laura Santoni et al.
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
by Maria Laura Santoni, Elena Raponi, Aneta Neumann, Frank Neumann, Mike Preuss, Carola Doerr
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method aims to identify a diverse set of solutions that meet specific criteria, addressing the issue of users preferring multiple high-quality options over one optimal solution. Building upon existing techniques like evolutionary diversity optimization, quality diversity, and multimodal optimization, this paper introduces a novel approach for selecting a fixed number of solutions with a specified pairwise distance threshold while maximizing their average quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to find diverse solutions that users can compare and explore further based on different criteria. The goal is to identify a certain number of solutions that are far apart from each other, while still being high-quality overall. |
Keywords
» Artificial intelligence » Optimization
