Summary of Benchmarking Symbolic Regression Constant Optimization Schemes, by L.g.a Dos Reis et al.
Benchmarking symbolic regression constant optimization schemes
by L.G.A dos Reis, V.L.P.S. Caminha, T.J.P.Penna
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper evaluates eight parameter optimization methods used during evolutionary search in genetic programming approaches to symbolic regression. The authors compare these methods on ten benchmark problems, considering two different scenarios. They also propose using Tree Edit Distance (TED) as a metric for evaluating symbolic accuracy, in addition to classical error measures. The results show that different methods perform better in specific scenarios, and there is no single best choice for every problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Symbolic regression is a way for machines to learn from data without needing to see the exact answers. It’s like how humans can learn new things by trial and error. Researchers have been improving this technique using “genetic programming” approaches. They’ve found that getting better at adjusting parameters during the learning process helps symbolic regression work better. However, different scientists use different methods for adjusting these parameters, and there isn’t a clear winner. In this paper, researchers test eight different parameter optimization methods on ten problem sets. They also come up with a new way to measure how well these methods are working. The results show that each method is good at solving certain problems, but none is perfect. |
Keywords
» Artificial intelligence » Optimization » Regression
