### 🚀 Performance (CPU)
* Replaces `np.polyval` with a parallel Numba JIT function (`_calculate_ranks_numba`).
* Replaces $O(N \log N)$ `np.argsort` with $O(N)$ `np.argpartition` in the GA loop.
* Adds `numba` as a core dependency.
### 🧠 Robustness (Algorithm)
* Implements Blend Crossover (BLX-$\alpha$) for better, extrapolative exploration.
* Uses a hybrid selection model (top X% for crossover, 100% for mutation) to preserve root niches.
* Adds `selection_percentile` and `blend_alpha` to `GA_Options` for tuning.
This commit introduces a major enhancement to the genetic algorithm's convergence logic and refactors key parts of the API for better clarity and usability.
- **feat(ga):** Re-implements the GA solver (CPU & CUDA) to use a more robust strategy based on Elitism, Crossover, and Mutation. This replaces the previous, less efficient model and is designed to significantly improve accuracy and convergence speed.
- **feat(api):** Updates `GA_Options` to expose the new GA strategy parameters:
- Renames `mutation_percentage` to `mutation_strength` for clarity.
- Adds `elite_ratio`, `crossover_ratio`, and `mutation_ratio`.
- Includes a `__post_init__` validator to ensure ratios are valid.
- **refactor(api):** Moves `quadratic_solve` from a standalone function to a method of the `Function` class (`f1.quadratic_solve()`). This provides a cleaner, more object-oriented API.
- **docs:** Updates the README, `GA_Options` doc page, and `quadratic_solve` doc page to reflect all API changes, new parameters, and updated usage examples.
- **chore:** Bumps version to 0.4.0.
Reviewed-on: #16
Co-authored-by: Jonathan Rampersad <rampersad.jonathan@gmail.com>
Co-committed-by: Jonathan Rampersad <rampersad.jonathan@gmail.com>
