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//! # 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: //! //! ```ignore //! [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 //! //! ```ignore //! // 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 //! //! ```ignore //! // 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. //! let result = s.run(); //! // This will fail if the result was an error: //! let best = result.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 //! }; //! ``` extern crate rand; extern crate time; /// Contains the definition of a Phenotype. pub mod pheno; /// Contains implementations of Simulators, which can run genetic algorithms. pub mod sim;