GMAB is a cutting-edge optimization algorithm that merges genetic algorithms and multi-armed bandit strategies to efficiently solve stochastic problems.
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.
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)Pull requests are welcome. For major changes, please open an issue first to discuss what you want to change.
tbd
Deniz Preil wrote the initial GMAB code, which Timo Kühne and Jonathan Laib rewrote.
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.