GMAB is a cutting-edge optimization algorithm that merges genetic algorithms and multi-armed bandit strategies to efficiently solve stochastic problems.

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GMAB (Genetic Multi-Armed Bandits) is an innovative optimization algorithm designed to tackle stochastic problems with high efficiency. By combining genetic algorithms with multi-armed bandit mechanisms, GMAB offers a unique, reinforcement learning-based approach to solving complex, large-scale optimization issues. Whether you're working in operations research, machine learning, or data science, GMAB provides a robust, scalable solution for optimizing your stochastic models.

Usage

To install GMAB, add the following to your Cargo.toml file:

[dependencies]
gmab = { git = "https://github.com/E-MAB/G-MAB.git" }

use gmab::gmab::Gmab;

fn your_function(x: &[f64]) -> f64 {
    // your function here
}

fn main() {
    let bounds = vec![(1, 100), (1, 100)];
    let mut gmab = Gmab::new(your_function, bounds);
    let evaluation_budget = 10000;
    let result = gmab.optimize(eval_budget);
    println!("Result: {:?}", result);
}

from gmab import Gmab

def test_function(number: list) -> float:
    # your function here

if __name__ == '__main__':
    bounds = [(-5, 10), (-5, 10)]
    gmab = Gmab(test_function, bounds)
    evaluation_budget = 10000
    result = gmab.optimize(evaluation_budget)
    print(result)

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you want to change.

License

tbd

Credit

Deniz Preil wrote the initial GMAB code, which Timo Kühne and Jonathan Laib rewrote.

Citing GMAB

If you use GMAB in your research, please cite the following paper:

Preil, D., & Krapp, M. (2024). Genetic Multi-Armed Bandits: A Reinforcement Learning Inspired Approach for Simulation Optimization. IEEE Transactions on Evolutionary Computation.