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.

Installation

You can use this library by adding the following lines to your Cargo.toml file:

[dependencies]
rsgenetic = "0.8"

and adding extern crate rsgenetic; to your crate root.

Features

Available Simulators

There is currently only one, sequential, simulator. This simulator will run the genetic algorithm on a single thread.

Available Selection Types

There are currently four selection types available:

• Maximize
• Tournament
• Stochastic
• Roulette

There is a short explanation for each of these below. Currently, the number of parents may vary depending on the chosen selection type. For more information, look at the documentation for the SelectionType enum.

Maximize

Maximize takes 1 parameter: the count. This is half the number of parents that will be selected. Selection happens by taking the top count * 2 individuals, ranked by fitness. The resulting number of parents is count * 2.

Tournament

Tournament takes 2 parameters: the number of tournaments and the count. The count indicates how many phenotypes participate in a tournament. The resulting number of parents is num * 2.

Stochastic

Stochastic takes 1 parameter: the count. The resulting number of parents is count.

Roulette

Roulette takes 1 parameter: the count. The resulting number of parents is count.

Early Stopping

If you wish, you can stop early if the fitness value of the best performing Phenotype doesn't improve by a large amount for a number of iterations. This can be done by calling the set_early_stop(delta: f64, n_iters: u32) function on the SimulatorBuilder.

Examples

Implementing Phenotype

// 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.
// This is required for the internal workings of the library.
impl Clone for Test {
    fn clone(&self) -> Self {
        Test { i: self.i }
    }
}

Running a Simulation

// 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 using a builder.
let mut s = *seq::Simulator::builder(tests) // Population is mandatory
                  .set_max_iters(1000)
                  .set_selection_type(sim::SelectionType::Tournament {
                        count: 3,
                        num: 5
                  })
                  .set_fitness_type(sim::FitnessType::Minimize)
                  .build();
// We can now run the simulator.
s.run();
// This will fail if the result was an error:
let best = s.get().unwrap();
// For this simple example, we should always get 0.
assert!((*best).i == 0);
// We can also get the time spent running:
let time = match s.time() {
    Some(x) => x, // Contains the time in ns
    None    => -1 // Overflow occured
};

Modules

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