I am trying to solve a NQueen problem using Evolutionary algorithm but im not able to get desired output of my graph, Following is the code I wrote import matplotlib.pyplot as plt import random import numpy as np # Constants N = 8 # Size of the N-Queens problem POP_SIZE = 100 # Population size GENERATIONS = 1000 # Number of generations MUTATION_PROBABILITIES = [0.6, 0.8, 1.0] # Mutation probabilities # Fitness function to maximize non-attacking pairs def fitness(solution): non_attacking_pairs = 0 for i in range(N): for j in range(i + 1, N): if solution != solution[j] and abs(i - j) != abs(solution - solution[j]): non_attacking_pairs += 1 return non_attacking_pairs # Return the number of non-attacking pairs def generate_population(): return [random.sample(range(N), N) for _ in range(POP_SIZE)] def select_parents(population): # Tournament selection for parents tournament = random.sample(population, 5) parent = max(tournament, key=fitness) # Maximizing fitness return parent def crossover(parent1, parent2): point = random.randint(1, N - 1) child1 = parent1[oint] + [x for x in parent2 if x not in parent1[oint]] child2 = parent2[oint] + [x for x in parent1 if x not in parent2[oint]] return child1, child2 def mutate(solution, mutation_rate): if random.random() < mutation_rate: i, j = random.sample(range(N), 2) solution, solution[j] = solution[j], solution return solution def next_generation(population, mutation_rate): new_population = [] # Sort the population by fitness in descending order sorted_population = sorted(population, key=fitness, reverse=True) # Select parents and create the new population while len(new_population) < POP_SIZE: parent1 = select_parents(sorted_population) parent2 = select_parents(sorted_population) child1, child2 = crossover(parent1, parent2) # Mutate the children new_population.append(mutate(child1, mutation_rate)) new_population.append(mutate(child2, mutation_rate)) return new_population # Evolutionary Algorithm with tracking of fitness values def evolutionary_algorithm(mutation_rate): population = generate_population() best_fitness_values = [] average_fitness_values = [] for generation in range(GENERATIONS): # Evaluate fitness of current population fitness_values = [fitness(individual) for individual in population] best_fitness = max(fitness_values) # Maximization average_fitness = np.mean(fitness_values) # Record best and average fitness best_fitness_values.append(float(best_fitness)) # Keep as float average_fitness_values.append(float(average_fitness)) # Keep as float # Generate the next generation population = next_generation(population, mutation_rate) return best_fitness_values, average_fitness_values # Store average fitness values across mutation rates overall_average_fitness = np.zeros(GENERATIONS) # Run the evolutionary algorithm for each mutation probability for mutation_rate in MUTATION_PROBABILITIES: best_fitness, average_fitness = evolutionary_algorithm(mutation_rate) # Accumulate average fitness values overall_average_fitness += np.array(average_fitness) # Calculate the overall average fitness across mutation rates overall_average_fitness /= len(MUTATION_PROBABILITIES) # overall_average_fitness = np.round(overall_average_fitness) # Plotting the results plt.figure(figsize=(12, 6)) plt.plot(range(GENERATIONS), overall_average_fitness, label='Overall Average Fitness', color='blue') plt.title('Overall Average Non-Attacking Pairs Fitness Over Generations') plt.xlabel('Generation') plt.ylabel('Fitness (Number of Non-Attacking Pairs)') plt.xticks(ticks=np.arange(0, GENERATIONS + 1, 100)) # Set x-axis ticks at intervals of 10 plt.grid() plt.axhline(y=N * (N - 1) / 2 + 0.001, color='black', linewidth=0.1, linestyle='--', label='Max Non-Attacking Pairs') # Max pairs line plt.legend() plt.show() I tried changing population size, generation value but i still not able to get the smooth curve My Output:My Output Expected output fitness over generation: Expected output Curve How do i make that curve look smooth Continue reading...
