A post showing how a genetic algorithm when used appropriately can be used as a powerful means to solve the n-Queens problem of increasing sizes. A downloadable Visual Studio 2010 C++ project implementing the genetic algorithm is available.

**Problem Description**

The N-Queens problem is the placement of queens on a chess board so that none are threatened – no single queen share a common row, column, or diagonal. The difficulty of the problem explodes with the number of queens involved and is known to be computation expensive. For example, there are 4,426,165,368 possible arrangements of eight queens on an 8×8 board, but only 92 solutions (source: Wikipedia).

**Applying the genetic operators**

An outline of the genetic algorithm that was applied to this problem and implemented in C++ is as follows:

Generate a population of solutions representing the positions of the N number of queens on the chessboard. Solutions (“chromosomes”) are represented using integer arrays with N number of row positions. Each row position is generated randomly for each column, representing a configuration of queens on the board. For example for a board of size 8×8, the array [6, 3, 1, 7, 4, 8, 5, 2] represents one possible solution in the population.

Sample C++ code representing the Chromosome class is as follows. I make use of smart pointers (std::unique_ptr) to implement variable integer arrays representing the queen positions:

#pragma once #include <memory> class Chromosome { public: Chromosome(const int& size); ~Chromosome(void); void SetChromosome( const int& index, const unsigned char& value ); unsigned char GetChromosome( const int& index ); float GetChromosomeFitness(); void SetFitness( const float& fitness ); int size() const; void Print( const int& index ) const; private: std::unique_ptr<int[]> chr; float fitness; int chrSize; bool constraintViolated; };

Once we have generated the initial population of integer arrays representing the queen positions, we apply crossover to arbitrary selected pairs of solutions in the population, at a given probability, the crossover probability. Two solutions (‘parents’) are selected, and crossover is applied between randomly selected portions of the arrays, in order to produce two new (and possibly better) ‘child’ solutions. Here is how the GeneticAlgorithm class implements the two-point crossover utilized in this algorithm:

void GeneticAlgorithm::Crossover() { for ( int i = 0; i < populationSize; i++ ) { int r = rand() % 100; if ( r < crossoverRate ) { // Select random pair for crossover int index1 = rand() % populationSize; int index2 = index1 < populationSize - 1 ? index1 + 1 : 0; // Get crossover points int point1 = rand() % chromosomeSize; int point2 = rand() % chromosomeSize; while ( point1 == point2 ) { point2 = rand() % chromosomeSize; } if ( point1 > point2 ) { int tmp = point1; point1 = point2; point2 = tmp; } // Do 2-point crossover pop.Crossover( index1, index2, point1, point2 ); } } }

With a probability typically much smaller than that of crossover, mutation is applied to individually selected array values in order to modify existing solutions. Here is how mutation is applied to individually selected chromosome members:

void Population::Mutation( const int& index, const int& mutationRate ) { Chromosome* chr = pop.at( index ); for ( int i = 0; i < chrSize; i++ ) { int r = rand() % 1000; if ( r < mutationRate && i != bestMember) { unsigned char value = chr->GetChromosome( i ); unsigned char new_value = rand() % chr->size(); while (new_value == value) { new_value = rand() % chr->size(); } chr->SetChromosome( i, new_value ); } } }

Using a mechanism based on survival of the fittest, we select members of the population for inclusion into the new generation based on their ‘fitness’. For this problem, fitness is inversely proportional to the number of times each queen is threatened by the other queens on the board ie fitness = 1.0 / ( 1.0 + number_threats ). A solution where there are zero conflicts will have a perfect fitness score of 1.0. For this implementation a binary ‘tournament’ selection mechanism was used whereby arbitrarily selected pairs of solutions are selected, the one with the greater fitness being the winner and selected to join the subsequent population.

C++ implementation of selection mechanism as shown:

void GeneticAlgorithm::Select() { // For each pair of chromosomes selected int i = 0; while ( i < tournamentSize ) { // Get the chromosome pair for tournament selection int index1 = rand() % populationSize; int index2 = rand() % populationSize; while ( index1 == index2 ) { index2 = rand() % populationSize; } float fitness1 = fabs( pop.GetChromosomeFitness( index1 ) ); float fitness2 = fabs( pop.GetChromosomeFitness( index2 ) ); if ( fitness1 > fitness2 ) { // Copy chromosome 1 elements into chromosome 2 pop.CopyChromosome( index1, index2 ); } else if ( fitness1 < fitness2 ) { // Copy chromosome 2 elements into chromosome 1 pop.CopyChromosome( index2, index1 ); } i++; } }

Keep repeating these steps (crossover/mutation/evaluation/selection) until no further improvement is found.

**Experimental results**

Some experimental results obtained on various problem sizes. To set the problem size I tweak the ‘nQueens’ parameter in nQueensDlg.cpp:

const int nQueens = 8;

**8 Queens**

Observe how a solution with fitness = 1.0 contains no threatened queens, diagonally, horizontally or vertically:

**15 Queens**

**40 Queens**

**60 Queens**

In this experiment, an optimal solution was obtainable using the following parameters:

crossover rate = 70;

mutation rate = 0.5;

population size = 650;

tournament size = population_size / 5;

I also removed the painting of the black squares in the code for this particular run so that we may see the positions of the queens more clearly:

**Download Visual Studio C++ project**

Download the following Visual Studio 2010 project from the following Selz link. Very easy to use: