Summary of A Comparison Of Recent Algorithms For Symbolic Regression to Genetic Programming, by Yousef A. Radwan et al.
A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming
by Yousef A. Radwan, Gabriel Kronberger, Stephan Winkler
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel machine learning approach, symbolic regression, seeks to produce interpretable results by mapping data in a way that can be understood by scientists. This method differs from opaque approaches like random forests and neural networks. Recent advancements aim to combine the strengths of neural networks and deep learning with symbolic regression’s explanatory power. The paper evaluates emerging systems and compares an end-to-end transformer model for symbolic regression with traditional methods based on genetic programming, which have driven symbolic regression development over the years. Performance is tested on novel datasets to avoid bias towards older methods improved on well-known benchmark datasets. Results show that traditional GP methods remain superior to two recently published symbolic regression methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Symbolic regression is a way for machines to learn from data and make predictions, but also explain how they made those predictions. It’s like trying to read the mind of a machine! Traditionally, machine learning methods are not transparent, making it hard to understand why they make certain decisions. Symbolic regression tries to change that by creating models that can be understood by humans. The paper compares new and old ways of doing symbolic regression using something called end-to-end transformer models versus traditional genetic programming methods. They test these systems on fresh datasets to see which ones work best. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Regression » Transformer
