primordial-opt 0.1.4

Genetic algorithm library with composable selection, crossover, and mutation operators
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# primordial-opt

[![Crates.io](https://img.shields.io/crates/v/primordial-opt.svg)](https://crates.io/crates/primordial-opt)
[![Documentation](https://docs.rs/primordial-opt/badge.svg)](https://docs.rs/primordial-opt)
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[![License](https://img.shields.io/crates/l/primordial-opt.svg)](https://github.com/S4H/primordial-opt#license)

A genetic algorithm library for Rust, providing composable building blocks for evolutionary optimization.

## Features

- **Chromosome**: Binary representation with fitness caching
- **Selection**: `rank`, `tournament`, `roulette`, and `nsga2` (multi-objective)
- **Crossover**: `single_point`
- **Mutation**: `bit_flip`, `fill_random`

All selection methods return iterators, enabling flexible composition with `.take(n)`.

## Installation

```toml
[dependencies]
primordial-opt = "0.1"
```

## Quick Example

```rust
use primordial_opt::{Chromosome, selection, crossover, mutation};
use rand::SeedableRng;

let mut rng = rand::rngs::StdRng::seed_from_u64(42);

// Create and evaluate population
let mut population: Vec<Chromosome> = (0..100)
    .map(|_| Chromosome::new(64, &mut rng))
    .collect();

for c in &mut population {
    let fitness = evaluate(c); // your fitness function
    c.set_fitness(vec![fitness]);
}

// Select top 50 by rank
let parents: Vec<_> = selection::rank(&population).take(50).collect();

// Crossover
let (mut child1, mut child2) = crossover::single_point(&parents[0], &parents[1], &mut rng);

// Mutate
mutation::bit_flip(&mut child1, 0.01, &mut rng);
```

## Multi-Objective Optimization

Use NSGA-II for Pareto-based selection:

```rust
// Set multi-objective fitness
chromosome.set_fitness(vec![objective1, objective2]);

// NSGA-II sorts by Pareto front, then crowding distance
let selected: Vec<_> = selection::nsga2(&population).take(50).collect();
```

## Examples

- `cargo run --example rastrigin` - Single-objective optimization
- `cargo run --example zdt1` - Multi-objective optimization with NSGA-II

## License

MIT OR Apache-2.0