Generic Differential Evolution for Rust

Related tags

Command-line differential-evolution-rs
Overview

Differential Evolution Crates Version Build Status

Simple and powerful global optimization using a Self-Adapting Differential Evolution for Rust. See Wikipedia's article on Differential Evolution for more information.

Documentation: https://docs.rs/differential-evolution/*/differential_evolution/

Usage

Add this to your Cargo.toml:

[dependencies]
differential-evolution = "*"

and this to your crate root:

extern crate differential_evolution;

Examples

Differential Evolution is a global optimization algorithm that tries to iteratively improve candidate solutions with regards to a user-defined cost function.

This example finds the minimum of a simple 5-dimensional function.

extern crate differential_evolution;

use differential_evolution::self_adaptive_de;

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);
}

Similar Crates

License

Licensed under either of

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

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Comments
  • Use crate random choice for updating fitness

    Use crate random choice for updating fitness

    You may want to consider Rust Random Choice for updating your samples. It can handle directly float values and it is simpler to use than the rand crate.

    But the crate scores the costs diffenrently: The higher, the better. So you have to do this:

    let fitness_high = MAX_VALUE - fitness_low;
    opened by StefanoD 2
  • Constraints

    Constraints

    Hello Martinus,

    Great work on this crate. It is very fast... much faster than code I have written using 'Scipy minimize' in Python. I did notice though, it is rather unusable if we have constrained input parameters. It seems as though we pass in an initial min-max bound for each variable, but after that it is disregarded. Is there a way to keep these variables bounded? Or if not, can you point me to the section of code where this bound-breaking operation occurs? I would like to update it so I can use this great work you have done.

    -Chase

    opened by Chase1325 0
Owner
Martin Leitner-Ankerl
s/Martin Ankerl/Martin Leitner-Ankerl/
Martin Leitner-Ankerl
GitHub https://docs.rs/differential-evolution/
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