Fitness Functions Reference

Fitness functions evaluate solution quality.

Module

use fugue_evo::fitness::{Fitness, FitnessValue};
use fugue_evo::fitness::benchmarks::{Sphere, Rastrigin, Rosenbrock, Ackley};

Fitness Trait

pub trait Fitness<G>: Send + Sync {
    type Value: FitnessValue;

    fn evaluate(&self, genome: &G) -> Self::Value;
}

FitnessValue Trait

pub trait FitnessValue: Clone + PartialOrd + Send + Sync {
    fn to_f64(&self) -> f64;
}

Implemented for: f64, f32, i64, i32, ParetoFitness

Built-in Benchmarks

Sphere

let fitness = Sphere::new(dim);

Formula: f(x) = Σxᵢ²

Rastrigin

let fitness = Rastrigin::new(dim);

Formula: f(x) = 10n + Σ[xᵢ² - 10cos(2πxᵢ)]

Rosenbrock

let fitness = Rosenbrock::new(dim);

Formula: f(x) = Σ[100(xᵢ₊₁ - xᵢ²)² + (1 - xᵢ)²]

Ackley

let fitness = Ackley::new(dim);

ZDT Test Suite (Multi-Objective)

use fugue_evo::fitness::benchmarks::{Zdt1, Zdt2, Zdt3};

let fitness = Zdt1::new(30);  // 30 variables

Custom Fitness

Single Objective

struct MyFitness;

impl Fitness<RealVector> for MyFitness {
    type Value = f64;

    fn evaluate(&self, genome: &RealVector) -> f64 {
        // Higher = better
        let genes = genome.genes();
        -genes.iter().map(|x| x * x).sum::<f64>()
    }
}

Multi-Objective

struct MultiObjFitness;

impl Fitness<RealVector> for MultiObjFitness {
    type Value = ParetoFitness;

    fn evaluate(&self, genome: &RealVector) -> ParetoFitness {
        ParetoFitness::new(vec![obj1, obj2])
    }
}

CMA-ES Fitness

CMA-ES uses a separate trait (minimization):

pub trait CmaEsFitness: Send + Sync {
    fn evaluate(&self, x: &RealVector) -> f64;  // Returns value to MINIMIZE
}

See Also

• Custom Fitness Functions
• Multi-Objective Optimization