Struct Nsga2 

pub struct Nsga2 { /* private fields */ }

The optimizer’s settings: the variables, the number of goals, the population, the number of generations, and the crossover’s and mutation’s parameters. It serializes as its fields, and reads back through the same checks as Nsga2::new and its with_ methods.

Implementations

impl Nsga2

pub fn new( variables: Vec<Variable>, objectives: usize, ) -> Result<Self, AnalysisError>

The optimizer over variables for objectives goals, with the paper’s settings: a population of 100, 250 generations, crossover probability 0.9 and η_c = 20, mutation probability 1/n and η_m = 20.

Errors

AnalysisError::TooFew or AnalysisError::Count for no variables or more than MAX_VARIABLES, or no goals or more than MAX_OBJECTIVES; AnalysisError::DuplicateVariable for two variables of one name; AnalysisError::Domain for a variable without two finite bounds, or with one beyond MAX_BOUND; and AnalysisError::Unsupported for an integer variable.

pub fn with_population(self, size: usize) -> Result<Self, AnalysisError>

The same optimizer with a population of size: an even number, as children are bred two at a time, from 4 to MAX_POPULATION.

Errors

AnalysisError::TooFew below 4, AnalysisError::Count above MAX_POPULATION, and AnalysisError::Domain for an odd size.

pub fn with_generations(self, generations: usize) -> Result<Self, AnalysisError>

The same optimizer, run for generations generations, the first, random one included: it evaluates the population that many times.

Errors

AnalysisError::TooFew for none.

pub fn with_crossover( self, probability: f64, index: f64, ) -> Result<Self, AnalysisError>

The same optimizer with SBX crossing a pair of parents with probability, and distribution index index: the larger, the closer children fall to their parents.

Errors

AnalysisError::Domain for a probability outside [0, 1], or an index that is negative or not finite.

pub fn with_mutation( self, probability: f64, index: f64, ) -> Result<Self, AnalysisError>

The same optimizer with polynomial mutation changing each variable of a child with probability, and distribution index index: the larger, the smaller the changes.

Errors

As Nsga2::with_crossover.

pub fn variables(&self) -> &[Variable]

The variables.

pub fn objectives(&self) -> usize

The number of goals.

pub fn population(&self) -> usize

The population’s size.

pub fn generations(&self) -> usize

The number of generations.

pub fn start(&self, seed: u64) -> Run

Starts a run from seed: its first candidates are the random first population.

pub fn minimize_constrained( &self, seed: u64, model: impl FnMut(&[f64]) -> Goals, ) -> Result<Front, AnalysisError>

Runs NSGA-II on model under constraints from seed, evaluating each candidate in turn.

Errors

Run::tell_constrained’s.

pub fn minimize( &self, seed: u64, model: impl FnMut(&[f64]) -> Vec<f64>, ) -> Result<Front, AnalysisError>

Runs NSGA-II on model from seed, evaluating each candidate in turn: the model gives each candidate’s goals, every one to be made as small as it can be.

Errors

Run::tell’s.

Trait Implementations

impl Clone for Nsga2

fn clone(&self) -> Nsga2

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Nsga2

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Nsga2

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for Nsga2

fn eq(&self, other: &Nsga2) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for Nsga2

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for Nsga2