Crate rsgenetic

RsGenetic

RsGenetic provides a simple framework for genetic algorithms. You need to provide the definition of a Phenotype (also known as an Individual), define how crossover and mutation work, present a fitness function, choose some settings and this library takes care of the rest.

Example usage:

use std::cmp;
extern crate rsgenetic;
use rsgenetic::{pheno, sim};

// Define the structure of your Phenotype
struct Test {
    i: i32,
}

// Implement the Phenotype trait.
impl pheno::Phenotype for Test {
    fn fitness(&self) -> f64 {
        (self.i - 0).abs() as f64
    }

    fn crossover(&self, t: &Test) -> Test {
        Test { i: cmp::min(self.i, t.i) }
    }

    fn mutate(&self) -> Self {
        if self.i < 0 {
            Test { i: self.i + 1 }
        } else {
            Test { i: self.i - 1}
        }
    }
}

// Implement the Clone trait.
impl Clone for Test {
    fn clone(&self) -> Self {
        Test { i: self.i }
    }
}

// Generate a random population.
let mut tests: Vec<Box<Test>> = Vec::new();
for i in 0..100 {
    tests.push(Box::new(Test { i: i + 10 }));
}
// Create a simulator.
let mut s = sim::seq::Simulator::new(tests, // Population
                                     1000, // Iterations
                                     sim::seq::SelectionType::Tournament{
                                        count: 3,
                                        num: 5
                                     }, // Tournament Selection
                                     sim::seq::FitnessType::Minimize); // Minimize the fitness
s.run();
assert!((*s.get()).i == 0); // For this simple example, we should always get 0.

Modules

pheno	Contains the definition of a Phenotype.
sim	Contains implementations of Simulators, which can run genetic algorithms.