Easy_GA is a genetic algorithm library made for Rust projects. It provides full customization for your own genotypes definitions and a geneticAlgorithm implementation to wrap all the common logic within a genetic algorithm.
All the changes will be updated in the CHANGELOG.md
trait Gene: Definition to implement for your custom genotypes.trait Selection: Definition for your custom selection algorithms.
Roulette: Selection algorithm already implemented.Tournament: Selection algorithm implementation with n members on it.GeneticAlgorithm: The main class to wrap the business logic in the genetic algorithm execution.In your Cargo.tml you have to add the Easy_GA dependency
```rust
[dependencies]
easy_ga = "*"
Now I will show you a basic example of Easy_GA that you can find on main.rs
Files to include in order to use features:
```rust use easyga::Gene; // For defining our own gene. use easyga::GeneticAlgorithm; // To create a GeneticAlgorithm. use easy_ga::SelectionAlgorithms; // To specity a concrete SelectionAlgorithm.
Definition of a custom Gene implementing easy_ga::Gene trait:
```rust
struct MyGene {
// Fields.
fitness: f64 // Recomended to avoid recalculate fitness on get_fitness
}
impl Gene for MyGene { fn init() -> Self { // Gene constructor. }
fn calculate_fitness(&mut self) -> f64 {
// Fitness function.
}
fn crossover(&self, other: &Self) -> Self {
// Crossover implementation.
}
fn mutate(&mut self) {
// Mutation implementation.
}
fn get_fitness(&self) -> f64 {
// Returns the fitness
}
}
``
At this moment, we need to implement theClone&Copytraits for ourGene`. I will try to avoid that in a future versions.
Initialization of our GeneticAlgorithm:
rust
let genetic_algorithm = GeneticAlgorithm::<MyGene>::new()
.population_size(20)
.iterations(50)
.mutation_rate(0.10)
.selection_rate(0.90)
.selection_algorithm(Box::new(SelectionAlgorithms::Tournament(10)))
.fitness_goal(100.0)
.init().unwrap();
We have other ways to initializate our GeneticAlgorithm such as GeneticAlgorithm::new_with_values if we don't want the chain calling method.
Now that we have defined our genotype and have initializate our GeneticAlgorhtm we have 2 ways of running it:
GeneticAlgorithm::run: This method runs the algorithm until the end and returns a tuple with (Gene, StopCriteria) that represents the best Gene in the execution and the reason to stop the execution.
rust
let (gene, stop_criteria) = genetic_algorithm.run();
rust
while genetic_algorithm.is_running() {
let new_generation: &Vec<MyGene> = genetic_algorithm.next_iteration();
}
This is a personal side project mainly for me so any further implementations will be done in my spare time as a good way to teach me more about Rust.
Selection algorithmsEasy_GA is licensed under Mozilla Public License 2.0.