Struct CartesianGeneticProgramming 

pub struct CartesianGeneticProgramming<B: Backend> { /* private fields */ }

Classical Cartesian GP with (1 + λ) ES.

Example

use burn::backend::NdArray;
use rlevo_evolution::algorithms::gp_cgp::{CartesianGeneticProgramming, CgpConfig};

let strategy = CartesianGeneticProgramming::<NdArray>::new();
let params = CgpConfig::default_for(1);
assert!(params.genome_len() > 0);
let _ = strategy;

Implementations

impl<B: Backend> CartesianGeneticProgramming<B>

pub fn new() -> Self

Builds a new (stateless) strategy object.

Trait Implementations

impl<B: Clone + Backend> Clone for CartesianGeneticProgramming<B>

fn clone(&self) -> CartesianGeneticProgramming<B>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<B: Debug + Backend> Debug for CartesianGeneticProgramming<B>

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

Formats the value using the given formatter.

impl<B: Default + Backend> Default for CartesianGeneticProgramming<B>

fn default() -> CartesianGeneticProgramming<B>

Returns the “default value” for a type.

impl<B: Backend> Strategy<B> for CartesianGeneticProgramming<B>

where B::Device: Clone,

type Params = CgpConfig

Static parameters for a run.

type State = CgpState<B>

Generation-to-generation state.

type Genome = Tensor<B, 2, Int>

Genome container produced by ask.

fn init( &self, params: &CgpConfig, rng: &mut dyn Rng, device: &B::Device, ) -> CgpState<B>

Build the initial state.

fn ask( &self, params: &CgpConfig, state: &CgpState<B>, rng: &mut dyn Rng, device: &B::Device, ) -> (Tensor<B, 2, Int>, CgpState<B>)

Propose the next population.

fn tell( &self, _params: &CgpConfig, offspring: Tensor<B, 2, Int>, fitness: Tensor<B, 1>, state: CgpState<B>, _rng: &mut dyn Rng, ) -> (CgpState<B>, StrategyMetrics)

Consume fitness values and produce the next state.

fn best(&self, state: &CgpState<B>) -> Option<(Tensor<B, 2, Int>, f32)>

Best-so-far accessor.

impl<B: Copy + Backend> Copy for CartesianGeneticProgramming<B>