Genetic Algorithm: N-Queens

Evolution-Based Optimization Step-by-Step

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Genetic Algorithm Overview

Genetic Algorithms are inspired by natural evolution and use a population of solutions instead of a single solution. Solutions evolve over generations through selection, crossover, and mutation.

N-Queens Representation:
  • Chromosome: Array [2, 4, 1, 3] = queen positions
  • Col 1 → Row 2, Col 2 → Row 4, etc.
  • Fitness: Number of non-attacking pairs
  • Max Fitness: n(n-1)/2 pairs
GA Operations:
  • Selection: Choose parents by fitness
  • Crossover: Combine parent chromosomes
  • Mutation: Random small changes
  • Evolution: Iterate over generations
Want to understand selection methods better? Try our interactive Selection Methods demo

Best Solution Found

[?, ?, ?, ?]
0
Best Fitness
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Conflicts
0
Found in Gen

Algorithm Controls

4-20 individuals
0.01-0.5 probability
Set parameters and click "Initialize Population" to start the genetic algorithm.

Current Population (Generation 0)

Each card shows one solution (individual): Chromosome = queen positions, Chessboard = visual representation, Fitness = how good the solution is (higher = better, max = 6)
Population initialized. Click "Next Generation" to start evolution.
0
Generation
0
Population
0
Avg Fitness
6
Max Fitness
Fitness Evolution

Algorithm Execution Log

Initialization

Waiting for population initialization...