Crate primordial_opt 

Genetic algorithm library with composable selection, crossover, and mutation operators.

Overview

primordial-opt provides building blocks for evolutionary optimization:

• Chromosome - Binary representation with fitness caching
• selection - Parent selection (rank, tournament, roulette, nsga2)
• crossover - Recombination operators (single_point)
• mutation - Mutation operators (bit_flip, fill_random)
• run_ga - High-level GA runner with configurable parameters

Quick Start

Use run_ga for a complete GA loop:

use primordial_opt::{run_ga, GaConfig};

let result = run_ga(
    &GaConfig::default().population_size(100).generations(500),
    16,  // chromosome size in bytes
    |c| vec![fitness(c)],  // fitness function
    |p| println!("Gen {}", p.generation),  // progress callback
    &mut rng,
);

Low-Level API

For custom GA loops, use the individual operators:

use primordial_opt::{Chromosome, selection, crossover, mutation};

let parents: Vec<_> = selection::rank(&population).take(50).collect();
let (mut c1, mut c2) = crossover::single_point(&parents[0], &parents[1], &mut rng);
mutation::bit_flip(&mut c1, 0.01, &mut rng);

Modules

crossover

Crossover operators for genetic recombination.

mutation

Mutation operators for introducing genetic variation.

selection

Selection operators for parent selection in genetic algorithms.

Structs

Chromosome

A binary chromosome for genetic algorithms.

GaConfig

Configuration for the genetic algorithm.

GaResult

Result of running the genetic algorithm.

Progress

Progress snapshot passed to the callback each generation.

Functions

run_ga

Runs a genetic algorithm loop.