https://github.com/maxreiss123/gep_sbp_

Gene Expression Programming for symbolic regression in Julia
https://github.com/maxreiss123/gep_sbp_

explainable-ai explainable-ml julia machine-learning scientific-machine-learning symbolic-regression

Last synced: 2 months ago
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Gene Expression Programming for symbolic regression in Julia

• Host: GitHub
• URL: https://github.com/maxreiss123/gep_sbp_
• Owner: maxreiss123
• Created: 2024-09-04T16:03:12.000Z (4 months ago)
• Default Branch: master
• Last Pushed: 2024-10-24T02:22:20.000Z (2 months ago)
• Last Synced: 2024-10-24T18:58:25.930Z (2 months ago)
• Topics: explainable-ai, explainable-ml, julia, machine-learning, scientific-machine-learning, symbolic-regression
• Language: Jupyter Notebook
• Homepage:
• Size: 3.36 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# VGeneExpressionProgramming for symbolic regression

The repository contains the implementation of the Gene Expression Programming[1] in conjunction with the semantic backpropagation approach developed in[2]. Here, the target is to reach dimensional homogeneity for physical dimensions through the cause of the exploration.

# How to use it?

- Install the package:

  ```julia

    julia --project=.

    using Pkg

    Pkg.add(url="https://github.com/maxreiss123/GEP_SBP_.git")

  ```

- Remark for your CSV file: Main_min_with_csv.jl in the test folder provides a step-by-step guide on how to initialize the GEP for your own problem

- Remark for your CSV file and utilizing dimensional homogeneity: Main_min_with_csv_and_units.jl in the test folder provides a step-by-step guide

- Remark: the tutorial folder contains notebook, that can be run with google-colab, while showing a step-by-step introduction

# References

- [1] Ferreira, C. (2001). Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems, 13.

- [2] Reissmann, M., Fang, Y., Ooi, A., & Sandberg, R. (2024). Constraining genetic symbolic regression via semantic backpropagation. arXiv. https://arxiv.org/abs/2409.07369

 # Acknowledgement

 - The Coefficient optimization is inspired by [https://github.com/MilesCranmer/SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/ConstantOptimization.jl)

 - We employ the insane fast [DynamicExpressions.jl](https://github.com/SymbolicML/DynamicExpressions.jl) for evaluating our expressions

# Todo 

- [ ] Documentation

- [ ] Naming conventions!

- [ ] Re-write postprocessing

- [ ] Improve usability for user interaction

- [ ] Next operations: Tail flip, Connection symbol flip, wrapper class for easy usage, config class for predefinition, staggered exploration