genevo 0.7.1

//! The `operator` module defines the types of genetic operators as traits.
//! A genetic operator defines a function that performs a specific stage in
//! the genetic algorithm. Each of these genetic operator can be implemented
//! in variety of ways using different algorithms and methods.
//!
//! The genetic operators are the building blocks of the genetic algorithm.
//! Their different implementations can be combined in a variety of ways to
//! make up the actual simulation of a specific problem.

pub mod prelude;

use crate::{
    algorithm::EvaluatedPopulation,
    genetic::{Children, Fitness, Genotype, Offspring, Parents},
    random::Rng,
};

/// Marker trait for genetic operators and functions that are used for
/// single-objective optimization.
pub trait SingleObjective {}

/// Marker trait for genetic operators and functions that are used for
/// multi-objective optimization.
pub trait MultiObjective {}

/// A `GeneticOperator` defines a function used to guide the genetic algorithm
/// towards a solution to a given problem. There are three main types of
/// operators - Selection, Crossover and Mutation - which must work in
/// conjunction with one another in order for the algorithm to be successful.
///
/// There are unary operators that operate on one genotype at a time, e.g.
/// mutation operators, and binary operators that work on two genotypes
/// at a time, e.g. crossover operators.
pub trait GeneticOperator: Clone {
    /// The name of the operator used for display purposes. The name should
    /// make clear to the user of the simulation which implementation of which
    /// kind of operator is being performed.
    ///
    /// It is recommended to combine some name of the method implemented by
    /// this operator (first part) with some name for the kind of operator
    /// (second part), e.g. "Flip-Bit-Mutation" or "Roulette-Wheel-Selection".
    fn name() -> String;
}

/// A `SelectionOp` defines the function of how to select solutions for being
/// the parents of the next generation.
pub trait SelectionOp<G, F>: GeneticOperator
where
    G: Genotype,
    F: Fitness,
{
    /// Selects individuals from the given population according to the
    /// implemented selection strategy.
    fn select_from<R>(
        &self,
        population: &EvaluatedPopulation<G, F>,
        rng: &mut R,
    ) -> Vec<Parents<G>>
    where
        R: Rng + Sized;
}

/// A `CrossoverOp` defines a function of how to crossover two
/// `genetic::Genotype`s, often called parent genotypes, to derive new
/// `genetic::Genotype`s. It is analogous to reproduction and biological
/// crossover. Cross over is a process of taking two parent solutions and
/// producing an offspring solution from them.
pub trait CrossoverOp<G>: GeneticOperator
where
    G: Genotype,
{
    /// Performs the crossover of the `genetic::Parents` and returns the result
    /// as a new vector of `genetic::Genotype` - the `genetic::Children`.
    fn crossover<R>(&self, parents: Parents<G>, rng: &mut R) -> Children<G>
    where
        R: Rng + Sized;
}

/// A `MutationOp` defines a function of how a `genetic::Genotype` mutates. It
/// is used to maintain genetic diversity from one generation of a population
/// of genetic algorithm genotypes to the next. It is analogous to biological
/// mutation. Mutation alters one or more gene values in a chromosome from its
/// initial state. In mutation, the solution may change entirely from the
/// previous solution. Hence GA can come to a better solution by using
/// mutation. Mutation occurs during evolution according to a user-definable
/// mutation probability. This probability should be set low. If it is set too
/// high, the search will turn into a primitive random search.
pub trait MutationOp<G>: GeneticOperator
where
    G: Genotype,
{
    /// Mutates the given 'Genotype' and returns it as a new 'Genotype'.
    fn mutate<R>(&self, genome: G, rng: &mut R) -> G
    where
        R: Rng + Sized;
}

/// A `ReinsertionOp` defines a function that combines the offspring with the
/// current population to create the population for the next generation.
/// At the end the new population must be of the same size as the original
/// population.
///
/// The `ReinsertionOp` may decide which individuals in the offspring will
/// make it into the new population. It is not required that all individuals
/// of the offspring are reinserted into the new population.
///
/// The only hard requirement is that the new population is of same size as
/// the original population. If the offspring contains less individuals than
/// the size of the original population or not all individuals are moved
/// to the new population then some individuals from the original population
/// are taken over into the new population. If the size of the offspring is
/// bigger than the size of the original population, then not all individuals
/// from the offspring are inserted into the new population.
pub trait ReinsertionOp<G, F>: GeneticOperator
where
    G: Genotype,
    F: Fitness,
{
    /// Combines the given offspring with the current population to create
    /// the population of the next generation.
    ///
    /// The offspring parameter is passed as mutable borrow. It can be
    /// mutated to avoid cloning. The `genetic::Genotype`s that make it up into
    /// the new population should be moved instead of cloned. After this
    /// function finishes the offspring vector should hold only those
    /// `genetic::Genotype`s that have not been included in the resulting
    /// population. If by the end of this function all `genetic::Genotype`s in
    /// offspring have been moved to the resulting population the offspring
    /// vector should be left empty.
    fn combine<R>(
        &self,
        offspring: &mut Offspring<G>,
        population: &EvaluatedPopulation<G, F>,
        rng: &mut R,
    ) -> Vec<G>
    where
        R: Rng + Sized;
}