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R Robust Regression Evolution

This example demonstrates how to use OpenEvolve with the R programming language. The example focuses on evolving robust regression algorithms that can handle outliers in data, showcasing OpenEvolve's ability to work with statistical computing languages beyond Python.

Files

  • initial_program.r: Starting R implementation with basic least squares regression
  • evaluator.py: Python evaluator that runs R code and measures performance
  • config.yaml: Configuration optimized for statistical algorithm evolution
  • requirements.txt: Dependencies for both R and Python components

Prerequisites

R Dependencies

Install R (version 3.6 or higher) and the required packages:

install.packages(c("jsonlite"))

Python Dependencies

pip install -r requirements.txt

Usage

Run the evolution process:

cd examples/r_robust_regression
python ../../openevolve-run.py initial_program.r evaluator.py --config config.yaml --iterations 100

This example shows how OpenEvolve can evolve algorithms in R by starting with a basic least squares implementation and improving it to handle outliers through various robust regression techniques.