Struct StochasticUniversalSampling 

pub struct StochasticUniversalSampling<R: Rng> { /* private fields */ }

Stochastic universal sampling selection operator

This struct implements SelectionOperator trati and can be used with GA

Note: This selection operator requires positive fitenss function. No runtime checks are performed to assert this invariant. If aggregated fitenss in whole population is <= the behaviour is undefined, implementation dependent and might change without any notice.

Note: The same individual can be selected multiple times

Individuals are selected in process similar to described below:

1. Individuals are laid on real axis, in order they appear in population, to interval [0, total_fitness]; each individual is represented by sub interval of lengths equal to its fitness
2. count virtual pointers are placed along interval [0, total_fitness]; distance between pointers d is total_fitness / mating_pool_size; first pointer position is selected randomly from interval [0, d]
3. Iterate over the pointers and select the individuals they point to

See the source code for implemenation details

Implementations

impl StochasticUniversalSampling<ThreadRng>

pub fn new() -> Self

Returns new instance of StochasticUniversalSampling selection operator with default RNG

impl<R: Rng> StochasticUniversalSampling<R>

pub fn with_rng(rng: R) -> Self

Returns new instance of StochasticUniversalSampling selection operator with custom RNG

Trait Implementations

impl<IndividualT: IndividualTrait<FitnessValueT = f64>, R: Rng> SelectionOperator<IndividualT> for StochasticUniversalSampling<R>

fn apply<'a>( &mut self, _metadata: &GAMetadata, population: &'a [IndividualT], count: usize, ) -> Vec<&'a IndividualT>

Returns a vector of references to individuals selected to mating pool

Note: This selection operator requires positive fitenss function. No runtime checks are performed to assert this invariant. If aggregated fitenss in whole population is <= the behaviour is undefined, implementation dependent and might change without any notice.

Note: The same individual can be selected multiple times

Individuals are selected in process similar to described below:

1. Individuals are laid on real axis, in order they appear in population, to interval [0, total_fitness]; each individual is represented by sub interval of lengths equal to its fitness
2. count virtual pointers are placed along interval [0, total_fitness]; distance between pointers d is total_fitness / mating_pool_size; first pointer position is selected randomly from interval [0, d]
3. Iterate over the pointers and select the individuals they point to

See the source code for implemenation details

Arguments

• metadata - crate::ga::GAMetadata information on current stage of the algorithm (iteration, elapsed time, etc.)
• population - individuals to choose mating pool from
• count - target number of individuals in mating pool