N-Queens Solver using Genetic Algorithm in Rust

This project implements a genetic algorithm to solve the classic N-Queens problem in Rust. The solver uses parallelism and multithreading to efficiently find a solution by evolving a population of potential solutions over multiple generations.

The N-Queens problem involves placing N queens on an N x N chessboard such that no two queens threaten each other. This project demonstrates how genetic algorithms can be used to solve combinatorial optimization problems.

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How It Works

1. Initialization: A population of random individuals (potential solutions) is generated.
2. Fitness Calculation: Each individual's fitness is calculated based on the number of non-attacking queen pairs.
3. Selection: Individuals are selected for reproduction based on their fitness.
4. Crossover: Selected individuals are combined to produce offspring.
5. Mutation: Offspring are mutated to introduce genetic diversity.
6. Evolution: The process repeats until a solution with maximum fitness is found or the generation limit is reached.

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Building

1. Clone the repository:

   git clone https://github.com/amirmtin/nqueens
   cd nqueens

2. Build the project:

   cargo build --release

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Usage

Run the solver with the following command:

nqueens --n 8 --population_size 1000 --mutation_rate 0.1 --generation_limit 5000

Command-Line Options

Option	Description	Default Value
-n, --n	Number of queens (N)	8
-p, --population_size	Size of the population	1000
-m, --mutation_rate	Mutation rate (probability)	0.1
-g, --generation_limit	Maximum number of generations	5000

Example Output

Max fitness: 28
Generation 0: Best fitness: 26
Generation 1: Best fitness: 26
Generation 2: Best fitness: 26
...
Generation 48: Best fitness: 26
Generation 49: Best fitness: 26
Generation 50: Best fitness: 28
Solution found in generation 50: [3, 7, 0, 2, 5, 1, 6, 4]
Solution:
.  .  .  ♛  .  .  .  .  

.  .  .  .  .  .  .  ♛  

♛  .  .  .  .  .  .  .  

.  .  ♛  .  .  .  .  .  

.  .  .  .  .  ♛  .  .  

.  ♛  .  .  .  .  .  .  

.  .  .  .  .  .  ♛  .  

.  .  .  .  ♛  .  .  . 

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Performance

The solver leverages Rust's concurrency features (rayon and mpsc) to parallelize the selection, crossover, and mutation steps. This significantly improves performance, especially for larger values of N.

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License

This project is licensed under the MIT License. See LICENSE for details.