Function self_adaptive_de 

pub fn self_adaptive_de<F>(
    min_max_pos: Vec<(f32, f32)>,
    cost_function: F,
) -> Population<F, XorShiftRng>
where
    F: Fn(&[f32]) -> f32,

Convenience function to create a fully configured self adaptive differential evolution population.

Examples found in repository

examples/simple.rs (lines 16-19)

fn main() {
    // create a self adaptive DE with an inital search area
    // from -10 to 10 in 5 dimensions.
    let mut de = self_adaptive_de(vec![(-10.0, 10.0); 5], |pos| {
        // cost function to minimize: sum of squares
        pos.iter().fold(0.0, |sum, x| sum + x*x)
    });

    // perform 10000 cost evaluations
    de.iter().nth(10000);
    
    // show the result
    let (cost, pos) = de.best().unwrap();
    println!("cost: {}", cost);
    println!("pos: {:?}", pos);
}

examples/rastrigin.rs (line 32)

fn main() {
    // command line args: dimension, number of evaluations
    let args: Vec<String> = env::args().collect();
    let dim = args[1].parse::<usize>().unwrap();

    // initial search space for each dimension
    let initial_min_max = vec![(-5.12, 5.12); dim];

    // initialize differential evolution
    let mut de = self_adaptive_de(initial_min_max, rastrigin);

    // perform optimization for a maximum of 100000 cost evaluations,
    // or until best cost is below 0.1.
    de.iter().take(100000).find(|&cost| cost < 0.1);

    // see what we've found
    println!("{} evaluations done", de.num_cost_evaluations());
    
    let (cost, pos) = de.best().unwrap();
    println!("{} best cost", cost);
    println!("{:?} best position", pos);
}