vitthalhumbe/genetic-n-queen

1. This repository is a single-script, self-contained implementation of a Genetic Algorithm for the N-Queen problem. There is no multi-package structure, backend/frontend split, or external service boundary; the entire system lives in genetic.py with README.md as documentation.

2. The core subsystem is the evolutionary search loop. It repeatedly evolves a population of candidate board states across generations until it finds a valid or sufficiently fit solution, or until a stopping condition is reached.

3. The main data model is a candidate solution representing one N-Queen board configuration. The README describes the usual GA terms—population, generation, fitness, selection, crossover, and mutation—which implies the script centers on transforming board-state candidates rather than using deterministic search.

4. Fitness evaluation is the primary scoring boundary in the architecture. Each candidate is measured by how well it satisfies the N-Queen constraints, and that score drives all later decisions in the pipeline.

5. Selection is the first transformation stage after evaluation. The algorithm chooses higher-fitness candidates as parents, creating a feedback loop where better board states are more likely to reproduce.

6. Crossover and mutation are the reproduction operators. Crossover combines parent candidates to produce offspring, and mutation introduces random variation so the search can escape local optima and continue exploring new board states.

7. Population management is the second major subsystem. The script maintains a fixed number of candidate solutions per generation, replacing or refreshing them as the algorithm iterates.

8. The repository also includes result tracking and visualization. The README shows a plot of average population fitness and best fitness per generation, so the script likely records metrics across generations and renders them at the end as an output artifact.

9. The overall control flow is linear and batch-oriented: initialize population, score candidates, select parents, generate offspring, mutate, form the next generation, and repeat. This makes the diagram simple: one loop with fitness feedback rather than multiple cooperating services.

10. There are no signs of persistence, networking, concurrency, or database interaction. The architecture is a pure computational algorithm with optional plotting/output, which means the key diagram elements are just the GA pipeline and its metric output.