├── .gitignore ├── .travis.yml ├── Cargo.lock ├── Cargo.toml ├── LICENSE ├── README.md └── src ├── algorithm.rs ├── individual.rs ├── main.rs └── problem.rs /.gitignore: -------------------------------------------------------------------------------- 1 | target 2 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: rust 2 | rust: 3 | - stable 4 | - beta 5 | - nightly 6 | sudo: false 7 | script: cargo run --verbose --release 8 | -------------------------------------------------------------------------------- /Cargo.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "ansi_term" 3 | version = "0.11.0" 4 | source = "registry+https://github.com/rust-lang/crates.io-index" 5 | dependencies = [ 6 | "winapi 0.3.4 (registry+https://github.com/rust-lang/crates.io-index)", 7 | ] 8 | 9 | [[package]] 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112 | "checksum winapi-i686-pc-windows-gnu 0.4.0 (registry+https://github.com/rust-lang/crates.io-index)" = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6" 113 | "checksum winapi-x86_64-pc-windows-gnu 0.4.0 (registry+https://github.com/rust-lang/crates.io-index)" = "712e227841d057c1ee1cd2fb22fa7e5a5461ae8e48fa2ca79ec42cfc1931183f" 114 | -------------------------------------------------------------------------------- /Cargo.toml: -------------------------------------------------------------------------------- 1 | [package] 2 | name = "rust-genetic-algorithm" 3 | version = "1.3.0" 4 | authors = ["Andrew Schwartzmeyer "] 5 | license = "AGPL" 6 | readme = "README.md" 7 | repository = "https://github.com/andschwa/rust-genetic-algorithm" 8 | homepage = "https://github.com/andschwa/rust-genetic-algorithm" 9 | description = "A genetic algorithm in Rust for Schwefel's function." 10 | 11 | [profile.dev] 12 | opt-level = 2 13 | 14 | [dependencies] 15 | clap = "0.10.5" 16 | rand = "0.4.2" 17 | time = "0.1.39" 18 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU AFFERO GENERAL PUBLIC LICENSE 2 | Version 3, 19 November 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU Affero General Public License is a free, copyleft license for 11 | software and other kinds of works, specifically designed to ensure 12 | cooperation with the community in the case of network server software. 13 | 14 | The licenses for most software and other practical works are designed 15 | to take away your freedom to share and change the works. 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If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Genetic Algorithm 2 | ================= 3 | 4 | [![Build Status](https://travis-ci.org/andschwa/rust-genetic-algorithm.svg?branch=master)](https://travis-ci.org/andschwa/rust-genetic-algorithm) 5 | 6 | A genetic algorithm in [Rust][] for the following [benchmark problems][]: 7 | 8 | * Ackley 9 | * Griewangk 10 | * Rastrigin 11 | * Rosenbrock 12 | * Schwefel 13 | * Sphere 14 | 15 | Usage: 16 | 17 | 1. Install [Rust][] 18 | 2. Build with `cargo build --release` 19 | 3. Search with `./target/release/rust-genetic-algorithm` 20 | 4. See further usage with `rust-genetic-algorithm --help` 21 | 22 | Based on my prior implementation in [C++][]. 23 | 24 | [benchmark problems]: https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume24/ortizboyer05a-html/node6.html 25 | [Rust]: http://www.rust-lang.org/ 26 | [C++]: https://github.com/andschwa/uidaho-cs472-project1 27 | -------------------------------------------------------------------------------- /src/algorithm.rs: -------------------------------------------------------------------------------- 1 | /// A genetic algorithm in Rust 2 | /// Copyright (C) 2015 Andrew Schwartzmeyer 3 | 4 | use Parameters; 5 | use Problem; 6 | use individual::Individual; 7 | use rand::{Rng, thread_rng}; 8 | use std::thread; 9 | use time::precise_time_s; 10 | 11 | pub struct Results { 12 | pub problem: Problem, 13 | pub parameters: Parameters, 14 | pub individual: Individual, 15 | pub iterations: usize, 16 | pub duration: f64 17 | } 18 | 19 | /// A genetic algorithm that searches for convergence to the given 20 | /// tolerance for the problem across the n-dimensional hypercube, 21 | /// using a population of individuals, up to a maximum iterations 22 | /// number of generations. 23 | pub fn search(problem: Problem, params: Parameters) -> Results { 24 | // get thread local random number generator 25 | let mut rng = thread_rng(); 26 | 27 | // initialize population of individuals 28 | let mut population: Vec<_> = (0..params.population).map(|_| { 29 | Individual::new(problem, params.dimension, &mut rng) 30 | }).collect(); 31 | 32 | // start timing the search 33 | let start_time = precise_time_s(); 34 | 35 | // search iterations number of generations 36 | for i in 0..params.iterations { 37 | // select, mutate, and crossover individuals for next generation 38 | let mut offspring: Vec = Vec::with_capacity(population.len()); 39 | for _ in 0..population.len()/2 { 40 | let (mut x, mut y) = (select(&population, params.selection, &mut rng), 41 | select(&population, params.selection, &mut rng)); 42 | x.mutate(params.mutation, &mut rng); 43 | y.mutate(params.mutation, &mut rng); 44 | Individual::crossover(&mut x, &mut y, params.crossover, &mut rng); 45 | offspring.push(x); 46 | offspring.push(y); 47 | } 48 | assert!(offspring.len() == population.len()); 49 | 50 | // replace 2 random individuals with elite of prior generation 51 | for _ in 0..params.elitism { 52 | if let Some(x) = population.iter().min() { 53 | offspring[rng.gen_range(0, population.len())] = x.clone(); 54 | } 55 | } 56 | 57 | // replace population with next generation 58 | population = offspring; 59 | 60 | // examine best individual for convergence 61 | if let Some(x) = population.iter().min() { 62 | if x.fitness < params.tolerance { 63 | return Results { 64 | problem: problem, parameters: params, 65 | individual: x.clone(), iterations: i, 66 | duration: precise_time_s() - start_time 67 | }; 68 | } 69 | // print verbose information 70 | if params.verbosity > 0 && i % 10 == 0 { 71 | let fitness = x.fitness; 72 | let solution = x.solution.clone(); 73 | thread::spawn(move || { 74 | if params.verbosity >= 1 { 75 | println!("{}th fitness {}", i, fitness); 76 | } 77 | if params.verbosity >= 2 { 78 | println!{"{:?}", solution}; 79 | } 80 | }); 81 | } 82 | } 83 | } 84 | if let Some(x) = population.iter().min() { 85 | Results { problem: problem, parameters: params, 86 | individual: x.clone(), iterations: params.iterations, 87 | duration: precise_time_s() - start_time } 88 | } else { 89 | unimplemented!(); 90 | } 91 | } 92 | 93 | /// Tournament selection from n random individuals 94 | fn select(population: &[Individual], n: usize, rng: &mut R) -> Individual { 95 | if let Some(selected) = (0..n).map(|_| rng.choose(population)).min() { 96 | selected.unwrap().clone() 97 | } else { 98 | unimplemented!(); 99 | } 100 | } 101 | -------------------------------------------------------------------------------- /src/individual.rs: -------------------------------------------------------------------------------- 1 | /// A genetic algorithm in Rust 2 | /// Copyright (C) 2015 Andrew Schwartzmeyer 3 | 4 | use Problem; 5 | use rand::Rng; 6 | use rand::distributions::IndependentSample; 7 | use std::cmp::{Eq, PartialEq, Ordering, PartialOrd}; 8 | use std::mem; 9 | 10 | /// An Orderable, Cloneable solution with a cached fitness 11 | #[derive(Clone)] 12 | pub struct Individual { 13 | pub solution: Vec, 14 | pub fitness: f64, 15 | problem: Problem, 16 | } 17 | 18 | impl Individual { 19 | /// Constructs a new Individual to solve Problem with n random values 20 | pub fn new(problem: Problem, dimension: usize, rng: &mut R) -> Self { 21 | let x: Vec<_> = (0..dimension).map(|_| { 22 | problem.domain_dist().ind_sample(rng) 23 | }).collect(); 24 | let fitness = problem.fitness(&x); 25 | Individual { solution: x, fitness: fitness, problem: problem } 26 | } 27 | 28 | /// Mutate with chance n a single gene to a new value in the 29 | /// problem's domain (a "jump" mutation). 30 | /// 31 | /// Fitness is NOT evaluated as it is ALWAYS done in `crossover()` 32 | pub fn mutate(&mut self, chance: f64, rng: &mut R) { 33 | if rng.gen_range(0_f64, 1_f64) < chance { 34 | let i = rng.gen_range(0, self.solution.len()); 35 | self.solution[i] = self.problem.domain_dist().ind_sample(rng); 36 | } 37 | } 38 | 39 | /// Performs two-point crossover with chance n to swap a random 40 | /// set of [0, dimension] genes between a pair of individuals. 41 | /// 42 | /// Fitness is ALWAYS evaluated because it is NOT done in mutate() 43 | pub fn crossover(x: &mut Individual, y: &mut Individual, 44 | chance: f64, rng: &mut R) { 45 | assert_eq!(x.problem, y.problem); 46 | if rng.gen_range(0_f64, 1_f64) < chance { 47 | let len = x.solution.len(); 48 | let (start, n) = (rng.gen_range(0, len), rng.gen_range(0, len)); 49 | for i in start..start + n { 50 | mem::swap(&mut x.solution[i % len], &mut y.solution[i % len]); 51 | } 52 | } 53 | x.fitness = x.problem.fitness(&x.solution); 54 | y.fitness = y.problem.fitness(&y.solution); 55 | } 56 | } 57 | 58 | impl Eq for Individual {} 59 | 60 | impl Ord for Individual { 61 | /// This dangerously delegates to `partial_cmp`; NaN will panic 62 | fn cmp(&self, other: &Self) -> Ordering { 63 | if let Some(result) = self.fitness.partial_cmp(&other.fitness) { 64 | return result; 65 | } 66 | unimplemented!(); 67 | } 68 | } 69 | 70 | impl PartialEq for Individual { 71 | /// This doesn't use `fitness.eq()` because it needs to be 72 | /// consistent with `Eq` 73 | fn eq(&self, other: &Individual) -> bool { 74 | if let Some(result) = self.fitness.partial_cmp(&other.fitness) { 75 | return result == Ordering::Equal; 76 | } 77 | unimplemented!(); 78 | } 79 | } 80 | 81 | impl PartialOrd for Individual { 82 | /// This delegates to `fitness.partial_cmp()` 83 | fn partial_cmp(&self, other: &Self) -> Option { 84 | self.fitness.partial_cmp(&other.fitness) 85 | } 86 | } 87 | -------------------------------------------------------------------------------- /src/main.rs: -------------------------------------------------------------------------------- 1 | /// A genetic algorithm in Rust 2 | /// Copyright (C) 2015 Andrew Schwartzmeyer 3 | 4 | #[macro_use] 5 | extern crate clap; 6 | extern crate rand; 7 | extern crate time; 8 | 9 | use clap::{App, Arg}; 10 | use std::thread; 11 | 12 | mod algorithm; 13 | mod individual; 14 | mod problem; 15 | 16 | arg_enum!{ 17 | #[derive(Debug, PartialEq, Eq, Copy, Clone)] 18 | pub enum Problem { 19 | Ackley, 20 | Griewangk, 21 | Rastrigin, 22 | Rosenbrock, 23 | Schwefel, 24 | Sphere 25 | } 26 | } 27 | 28 | #[derive(Debug, Copy, Clone)] 29 | pub struct Parameters { 30 | tolerance: f64, 31 | dimension: usize, 32 | population: usize, 33 | iterations: usize, 34 | selection: usize, 35 | elitism: usize, 36 | mutation: f64, 37 | crossover: f64, 38 | verbosity: usize 39 | } 40 | 41 | /// Setup and run algorithm to search for solution 42 | fn main() { 43 | let matches = App::new("rust-genetic-algorithm") 44 | .version(&crate_version!()[..]) 45 | .author("Andrew Schwartzmeyer ") 46 | .about("A genetic algorithm in Rust for various benchmark problems.") 47 | .arg(Arg::with_name("tolerance").short("t").long("tol").takes_value(true) 48 | .help("Sets the convergence tolerance (0.01)")) 49 | .arg(Arg::with_name("dimension").short("d").long("dim").takes_value(true) 50 | .help("Sets the dimension of the hypercube (30)")) 51 | .arg(Arg::with_name("population").short("p").long("pop").takes_value(true) 52 | .help("Sets the size of the population (512)")) 53 | .arg(Arg::with_name("iterations").short("i").long("iter").takes_value(true) 54 | .help("Sets maximum number of generations (10,000)")) 55 | .arg(Arg::with_name("selection").short("s").long("select").takes_value(true) 56 | .help("Sets the size of the tournament selection (4)")) 57 | .arg(Arg::with_name("elitism").short("e").long("elite").takes_value(true) 58 | .help("Sets the number of elite replacements (2)")) 59 | .arg(Arg::with_name("mutation").short("m").long("mut").takes_value(true) 60 | .help("Sets the chance of mutation (0.8)")) 61 | .arg(Arg::with_name("crossover").short("c").long("cross").takes_value(true) 62 | .help("Sets the chance of crossover (0.8)")) 63 | .arg(Arg::with_name("verbose").short("v").long("verbose").multiple(true) 64 | .help("Print fitness (1) and solution (2) every 10th generation")) 65 | .arg(Arg::with_name("problem").multiple(true) 66 | .help("The problems to solve: 67 | * Ackley 68 | * Griewangk 69 | * Rastrigin 70 | * Rosenbrock 71 | * Schwefel 72 | * Sphere")) 73 | .get_matches(); 74 | 75 | let problems = value_t!(matches.values_of("problem"), Problem) 76 | .unwrap_or(vec![Problem::Ackley, Problem::Griewangk, Problem::Rastrigin, 77 | Problem::Rosenbrock, Problem::Schwefel, Problem::Sphere]); 78 | 79 | let parameters = Parameters { 80 | tolerance: value_t!(matches.value_of("tolerance"), f64).unwrap_or(0.01_f64), 81 | dimension: value_t!(matches.value_of("dimension"), usize).unwrap_or(30), 82 | population: value_t!(matches.value_of("population"), usize).unwrap_or(512), 83 | iterations: value_t!(matches.value_of("iterations"), usize).unwrap_or(10000), 84 | selection: value_t!(matches.value_of("selection"), usize).unwrap_or(4), 85 | elitism: value_t!(matches.value_of("elitism"), usize).unwrap_or(2), 86 | mutation: value_t!(matches.value_of("mutation"), f64).unwrap_or(0.8_f64), 87 | crossover: value_t!(matches.value_of("crossover"), f64).unwrap_or(0.8_f64), 88 | verbosity: matches.occurrences_of("verbose") as usize 89 | }; 90 | 91 | let workers = problems.iter().map(|&problem| { 92 | thread::spawn(move || algorithm::search(problem, parameters)) 93 | }); 94 | 95 | for worker in workers { 96 | if let Ok(results) = worker.join() { 97 | println!("{} converged to {} after {} generations in {} seconds.", 98 | results.problem, results.individual.fitness, 99 | results.iterations, results.duration); 100 | println!("{:?}", results.individual.solution); 101 | } 102 | } 103 | } 104 | -------------------------------------------------------------------------------- /src/problem.rs: -------------------------------------------------------------------------------- 1 | /// A genetic algorithm in Rust 2 | /// Copyright (C) 2015 Andrew Schwartzmeyer 3 | 4 | use Problem; 5 | use rand::distributions::Range; 6 | use std::f64::consts; 7 | 8 | impl Problem { 9 | /// Fitness function for the evolutionary computation benchmark 10 | /// problems evaluated on the hybercube of dimension p, where 11 | /// f(x*) = 0. 12 | /// 13 | /// # [Problems](https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume24/ortizboyer05a-html/node6.html) 14 | /// 15 | /// * Ackley: 20+e-20*exp(-0.2*sqrt((1/p)*sum(x_i^2)))-exp((1/p)*sum(cos(2*pi*x_i))), x*=(0, ...) 16 | /// * Griewangk: 1+sum(x_i^2/4000)-prod(cos(x_i/sqrt(i))), x*=(0, ...) 17 | /// * Rastrigin: 10*p+sum(x_i^2-10*cos(2*pi*x_i)), x*=(0, ...) 18 | /// * Rosenbrock: sum(100*(x_(i+1)-x_i^2)^2+(x_i-1)^2), x*=(1, ...) 19 | /// * Schwefel: 418.9829*p+sum(x_i*sin(sqrt(abs(x_i)))), x*=(420.9687, ...) 20 | /// * Sphere: sum(x_i^2), x*=(0, ...) 21 | pub fn fitness(&self, x: &[f64]) -> f64 { 22 | let p = x.len() as f64; 23 | match *self { 24 | Problem::Ackley => { 25 | 20_f64 + consts::E - 20_f64 * 26 | (-0.2_f64 * (p.recip() * (x.iter().fold(0_f64, |sum, x| { 27 | sum + x.powi(2) 28 | })).sqrt())).exp() - 29 | (p.recip() * x.iter().fold(0_f64, |sum, x| { 30 | sum + (2_f64 * consts::PI * x).cos() 31 | })).exp()} 32 | Problem::Griewangk => { 33 | 1_f64 + x.iter().fold(0_f64, |sum, x| { 34 | sum + x.powi(2)/4000_f64 35 | }) - x.iter().enumerate().fold(1_f64, |prod, (i, x)| { 36 | prod * (x/((i + 1) as f64).sqrt()).cos() 37 | })} 38 | Problem::Rastrigin => { 39 | 10_f64 * p + x.iter().fold(0_f64, |sum, x| { 40 | sum + x.powi(2) - 10_f64 * (2_f64 * consts::PI * x).cos() 41 | })} 42 | Problem::Rosenbrock => { 43 | x.iter().skip(1).zip(x).fold(0_f64, |sum, (x_next, x)| { 44 | sum + 100_f64 * (x_next - x.powi(2)).powi(2) 45 | + (x - 1_f64).powi(2) 46 | })} 47 | Problem::Schwefel => { 48 | 418.9829_f64 * p + x.iter().fold(0_f64, |sum, i| { 49 | sum + i * i.abs().sqrt().sin() 50 | })} 51 | Problem::Sphere => { 52 | x.iter().fold(0_f64, |sum, x| sum + x.powi(2))} 53 | } 54 | } 55 | 56 | /// Domain for the given problem. 57 | pub fn domain(&self) -> (f64, f64) { 58 | match *self { 59 | Problem::Ackley => (-30_f64, 30_f64), 60 | Problem::Griewangk => (-600_f64, 600_f64), 61 | Problem::Rastrigin => (-5.12_f64, 5.12_f64), 62 | Problem::Rosenbrock => (-2.048_f64, 2.048_f64), 63 | Problem::Schwefel => (-512.03_f64, 511.97_f64), 64 | Problem::Sphere => (-5.12_f64, 5.12_f64), 65 | } 66 | } 67 | 68 | /// Random distribution for problem's domain 69 | pub fn domain_dist(&self) -> Range { 70 | let (a, b) = self.domain(); 71 | Range::new(a, b) 72 | } 73 | } 74 | --------------------------------------------------------------------------------