optimize

To speed up a Python function while preserving behavior, you can use an automated evolution pipeline that iteratively optimizes the target function and validates it against pytest tests to ensure correctness.

Optimizing a Python function requires a Python file containing the target function and a separate pytest-compatible test file to guide the evolution and validate that performance improvements maintain correctness.

Automated code evolution works by running a target Python function through preflight checks, building a performance harness, and executing configurable iterations to yield a faster variant and a diff against the original.

Yes, you can target a specific Python function for optimization by providing an optional function name parameter, allowing the evolution process to isolate and improve only the specified computational bottleneck.

After running the optimization pipeline, you receive the best-performing evolved variant of the target Python function, a diff comparing it to the original implementation, and detailed run outputs stored in a dedicated directory.