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Genetic Programming

🚧 Under Construction 🚧

As of 01/15/2026: These docs are a work in progress and may not be complete or fully accurate. Please check back later for updates.

Genetic Programming (GP) in Radiate enables the evolution of programs represented as expression trees and computational graphs. This powerful feature allows you to solve complex problems by evolving mathematical expressions, decision trees, neural network topologies, and more.

Radiate's GP implementation provides two core data structures: Trees for hierarchical expressions and Graphs for complex computational networks. Each offers unique capabilities for different problem domains. Both the tree and graph modules come with their own specific chromosomes, codecs and alters to evolve these structures effectively.

Installation

To use Radiate's Genetic Programming features, you need to install the library with the appropriate feature flags.

pip install radiate

cargo add radiate -F gp

# Or Cargo.toml
[dependencies]
radiate = { version = "x", features = ["gp", ...] }

Arity

Arity is the cornerstone concept behind how nodes and ops function within Radiate's genetic programming system. Arity is a term used to describe the number of arguments or inputs a function takes. In the context of genetic programming, arity defines how many inputs a node or an op can accept in both trees and graphs. For graphs arity is used to determine how many incoming connections a GraphNode can have, while for trees it determines how many children a TreeNode can have. Radiate uses an enum to express arity defined in three variants:

  1. Zero: The operation takes no inputs (e.g., constants).
  2. Exact(usize): The operation takes a specific number of inputs.
  3. Any: The operation can take any number of inputs (e.g., functions like sum or product).

In most cases, the tree or graph will try it's best ensure that their node's arity is not violated, but it will ultimately be up to the user to ensure that the arity is correct.