Crate rsgenetic [−] [src]
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. |