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sean p. may
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N-Queens genetic algorithm solution with convergence chart

N-Queens Genetic Algorithm

Updated Nov 2025

Git Repo(opens in new tab)
PythonGenetic Algorithms

this project explores the n-queens puzzle through a compact genetic algorithm. it's intentionally small and transparent, so it's easy to see how population size, mutation behavior, and iteration caps affect convergence.

tournament selection + single-point crossover

mutation schedule that scales with board size

CLI/.env configurable experiments

what i built

  • single-file genetic algorithm with tournament selection and crossover
  • collision-based fitness function for scoring candidate boards
  • CLI and .env configuration for quick experimentation

how it works

  1. 1initialize a population of candidate boards
  2. 2score conflicts and select parents via tournaments
  3. 3apply crossover and mutation across generations
  4. 4stop when a zero-collision solution is found or the iteration cap is hit

results

  • ✓nice compact reference implementation for an evolutionary search workflow

what's next

  • add seeded benchmarking and clearer example outputs
  • try alternate encodings or hybrid local-search ideas