Rpsge: Probabilistic Structured Grammatical Evolution in R 🧬

Overview 🔍

Rpsge implements Probabilistic Structured Grammatical Evolution (PSGE) in R. PSGE combines the structured representation from Structured Grammatical Evolution with probabilistic grammar learning, creating an adaptive approach to grammar-guided genetic programming.

Installation 📦

You can install the development version of Rpsge from GitHub:

# install.packages("devtools")
devtools::install_github("sjvrensburg/Rpsge")

Basic Usage 💻

Here's a simple example of solving a symbolic regression problem:

library(Rpsge)
library(progressr)

# Configure progress reporting
handlers(global = TRUE)
handlers("progress")

# Define fitness function
fitness_fn <- function(phenotype) {
  # Example: Try to evolve x^2 + x + 1
  f <- function(x) eval(parse(text = phenotype))
  x <- seq(-1, 1, by = 0.1)
  y_true <- x^2 + x + 1
  y_pred <- sapply(x, f)
  sqrt(mean((y_pred - y_true)^2))
}

# Read grammar file
grammar <- read_pcfg_grammar("path/to/grammar.bnf")

# Run PSGE with progress reporting
with_progress({
  result <- psge(
    grammar_file = "path/to/grammar.bnf",
    fitness_fn = fitness_fn,
    pop_size = 100,
    generations = 50
  )
})

# Print best solution
print(result$best_solution$phenotype)

Features ⭐

• Probabilistic grammar-guided evolution
• High-performance C++ core through Rcpp
• Parallel fitness evaluation support
• Comprehensive grammar validation
• Progress tracking and visualization

Citation 📚

If you use Rpsge in your research, please cite:

@inproceedings{megane2022psge,
  title={Probabilistic Structured Grammatical Evolution},
  author={M{\'e}gane, Jessica and Louren{\c{c}}o, Nuno and Machado, Penousal},
  booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)},
  pages={1--9},
  year={2022},
  doi={10.1109/CEC55065.2022.9870397}
}

Contributing 🤝

Contributions are welcome! Please feel free to submit a Pull Request.

License 📄

GPL (>= 3)