🧬 Genetic Algorithm - String Matching in Rust

This goal of this project build a simple genetic algorithm in Rust over a 48 hour period to familiarize myself with the language. the algorithm is meant to evolve a population of binary-encoded individuals to match a target ASCII string (e.g., "hello_world"). The algorithm uses crossover, mutation, and a fitness function based on string similarity to iteratively improve the population toward an ideal solution.

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📁 Project Structure

.
├── src
│   ├── main.rs              # Entry point demonstrating string matching
│   └── genetic_algorithm.rs # Implementation of the genetic algorithm

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🚀 Features

• ASCII string matching using genetic algorithms.
• Bitwise and bytewise crossover strategies.
• Multi-threaded population evolution using std::thread and num_cpus.
• Fitness function based on ASCII character similarity.
• Individuals represented as binary gene vectors.
• Configurable mutation rate and population parameters.

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🔧 How It Works

String Matching

The algorithm attempts to evolve a population to match a given ASCII string (e.g., "hello_world"). Each individual is a sequence of bool genes, grouped in 8s to represent ASCII characters.

Fitness Calculation:

fitness += 255 - abs(individual_byte - target_byte);

Higher fitness = closer ASCII match.

Genetic Operations

• Selection: Top N individuals are retained.
• Crossover: Each child's genes are built from random slices of parent genes (bitwise or bytewise).
• Mutation: Each gene has a configurable probability of flipping.

Termination

The loop stops when:

• A perfect match is found (fitness = 255 * len(string)), or
• You manually terminate (e.g., with Ctrl+C).

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🛠️ Usage

🧪 Run the Example

Make sure you have Rust installed, then run:

cargo run

This will evolve a population to match the string "hello_world".

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⚙️ Configuration

You can modify the following parameters in main.rs:

let goal_fitness = 12 * 255; // perfect match for "hello_world"
let gene_length = string.len() * 8;
let mut population = genetic_algorithm::init_population(
    gene_length,       // gene length (bytes * 8)
    10,                // population size
    4,                 // number of parents
    15,                // mutation rate (%)
    true,              // multi-threaded
    CrossoverType::Byte
);

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📦 Dependencies

Add these to your Cargo.toml:

[dependencies]
rand = "0.8"
num_cpus = "1.13"

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