Emil_Shanaty/Sudoku_solver
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emil
2024-11-27 21:14:04 +03:00
parent 65b3f31ba6
commit 681cd729bb
2 changed files with 30 additions and 16 deletions
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Main.java
+30 -7
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@@ -4,6 +4,13 @@ import java.util.List;
import java.util.Random;
public class Main {
// Initalizing variables
public static int POPULATIONSIZE = 0;
public static int TOURNAMENTSIZE = 0;
public static double MUTATIONRATE = 0;
// Threshold of number of mutable positions for easy level sudoku
public static int EASYTHRESHOLD = 60;
public static int HARDTHRESHOLD = 70;
// List to store the population of chromosomes
List<Chromosome> population = new ArrayList<>();
Random random = new Random();
@@ -39,10 +46,26 @@ public class Main {
}
Main mainInstance = new Main(); // Create instance of Main class
// Generate initial population of 100 chromosomes
mainInstance.generateInitialChromosomes(100, baseSudoku, mutablePositions);
double mutationRate = 0.05; // Mutation rate for genetic algorithm
// Choosing variables for different sudoku difficulties
if (mutablePositions.size() < EASYTHRESHOLD) { // Easy sudoku
POPULATIONSIZE = 75;
TOURNAMENTSIZE = 4;
MUTATIONRATE = 0.04;
} else if (mutablePositions.size() < HARDTHRESHOLD) { // Hard sudoku
POPULATIONSIZE = 150;
TOURNAMENTSIZE = 5;
MUTATIONRATE = 0.055;
}
else { // Ultra-hard sudoku
POPULATIONSIZE = 2000;
TOURNAMENTSIZE = 10;
MUTATIONRATE = 0.2;
}
// Generate initial population of 100 chromosomes
mainInstance.generateInitialChromosomes(POPULATIONSIZE, baseSudoku, mutablePositions);
Chromosome bestSolution = null;
int generation = 0; // Track the number of generations
@@ -53,14 +76,14 @@ public class Main {
List<Chromosome> newPopulation = new ArrayList<>();
for (int i = 0; i < mainInstance.population.size() / 2; i++) {
// Select parents using tournament selection
List<Chromosome> parents = mainInstance.tournamentSelection(5);
List<Chromosome> parents = mainInstance.tournamentSelection(TOURNAMENTSIZE);
// Perform crossover to create two children from the selected parents
Chromosome child1 = mainInstance.crossoverBySubgrids(parents.get(0), parents.get(1));
Chromosome child2 = mainInstance.crossoverBySubgrids(parents.get(1), parents.get(0));
// Apply mutation to both children
mainInstance.mutateChromosome(child1, mutationRate);
mainInstance.mutateChromosome(child2, mutationRate);
mainInstance.mutateChromosome(child1, MUTATIONRATE);
mainInstance.mutateChromosome(child2, MUTATIONRATE);
// Add both children to the new population
newPopulation.add(child1);
@@ -340,4 +363,4 @@ public class Main {
}
}
}
}
}