# lgp
Core Rust library for Linear Genetic Programming.
[](https://crates.io/crates/lgp)
[](../../LICENSE)
## Overview
`lgp` provides a trait-based framework for evolving sequences of register-based instructions to solve reinforcement learning and classification tasks. It includes built-in support for OpenAI Gym environments, Iris classification, and hybrid LGP + Q-Learning.
## Installation
Add to your `Cargo.toml`:
```toml
[dependencies]
lgp = "1.3"
```
To enable OpenAI Gym environment support:
```toml
[dependencies]
lgp = { version = "1.3", features = ["gym"] }
```
## Core Traits
The framework is built around these key traits:
- **`State`** - Represents an environment state with value access and action execution
- **`RlState`** - Extends `State` for RL environments with terminal state detection
- **`Core`** - Main trait defining the genetic algorithm components (registers, instructions, individuals)
- **`Fitness`** - Evaluates individual performance on a set of states
- **`Breed`** - Two-point crossover for creating offspring
- **`Mutate`** - Mutation operators for genetic variation
## Modules
| `core` | Core LGP engine — registers, instructions, individuals, and evolutionary loop |
| `problems` | Built-in problem implementations (CartPole, MountainCar, Iris) |
| `extensions` | Extended functionality including Q-Learning integration |
| `utils` | Utility functions for tracing, serialization, and configuration |
## Quick Example
```rust
use lgp::core::engines::core_engine::CoreEngine;
use lgp::problems::iris::IrisLgp;
fn main() {
// Configure and run an Iris classification experiment
let engine = CoreEngine::<IrisLgp>::builder()
.n_generations(100)
.n_individuals(100)
.build()
.unwrap();
engine.run();
}
```
See the [examples](../../examples/) directory and [extension guide](../../docs/EXTENDING.md) for more.
## Features
| `gym` | No | Enables OpenAI Gym environment support via `gymnasia` |
## Supported Environments
| CartPole | RL | 4 | 2 |
| MountainCar | RL | 2 | 3 |
| Iris | Classification | 4 | 3 |