diff --git a/job_shop_lib/genetic_algorithm.py b/job_shop_lib/genetic_algorithm.py new file mode 100644 index 00000000..4ffa3185 --- /dev/null +++ b/job_shop_lib/genetic_algorithm.py @@ -0,0 +1,115 @@ +# job_shop_lib/genetic_algorithm.py + +import random + +class GeneticAlgorithm: + """ + A class to encapsulate the logic for a Genetic Algorithm solver. + This class is unchanged from the previous version. + """ + def __init__(self, fitness_func, population_size, num_genes, + crossover_rate=0.8, mutation_rate=0.01, tournament_size=5): + self.fitness_func = fitness_func + self.population_size = population_size + self.num_genes = num_genes + self.crossover_rate = crossover_rate + self.mutation_rate = mutation_rate + self.tournament_size = tournament_size + self.population = self._initialize_population() + + def _initialize_population(self): + population = [] + for _ in range(self.population_size): + individual = [random.randint(0, 1) for _ in range(self.num_genes)] + population.append(individual) + return population + + def _selection(self): + tournament = random.sample(self.population, self.tournament_size) + best_individual = max(tournament, key=self.fitness_func) + return best_individual + + def _crossover(self, parent1, parent2): + if random.random() < self.crossover_rate: + point = random.randint(1, self.num_genes - 1) + child1 = parent1[:point] + parent2[point:] + child2 = parent2[:point] + parent1[point:] + return child1, child2 + return parent1, parent2 + + def _mutation(self, individual): + mutated_individual = [] + for gene in individual: + if random.random() < self.mutation_rate: + mutated_individual.append(1 - gene) + else: + mutated_individual.append(gene) + return mutated_individual + + def solve(self, num_generations): + best_overall_individual = None + best_overall_fitness = -float('inf') + + print("\nšŸ§¬ Starting Genetic Algorithm...") + for generation in range(num_generations): + fitness_scores = [self.fitness_func(ind) for ind in self.population] + best_fitness_in_gen = max(fitness_scores) + + if best_fitness_in_gen > best_overall_fitness: + best_overall_fitness = best_fitness_in_gen + best_index = fitness_scores.index(best_fitness_in_gen) + best_overall_individual = self.population[best_index] + + # Use \r to overwrite the line for a cleaner progress display + print(f"Generation {generation + 1}/{num_generations} | Best Fitness: {best_overall_fitness}", end='\r') + + print("\nāœ… Genetic Algorithm finished.") + return best_overall_individual, best_overall_fitness + + +# --- Helper function to get validated user input --- +def get_validated_input(prompt, target_type, min_val=None, max_val=None): + """A robust function to get and validate user input.""" + while True: + user_input = input(prompt) + try: + value = target_type(user_input) + if min_val is not None and value < min_val: + print(f"Error: Value must be at least {min_val}.") + elif max_val is not None and value > max_val: + print(f"Error: Value must be no more than {max_val}.") + else: + return value + except ValueError: + print(f"Error: Invalid input. Please enter a valid {target_type.__name__}.") + + +# --- Dynamic Example Usage --- +if __name__ == '__main__': + # Define a simple fitness function (One-Max problem) + def one_max_fitness(individual): + return sum(individual) + + print("--- āš™ļø Configure Genetic Algorithm Parameters ---") + + # Get user input for GA parameters + pop_size = get_validated_input("Enter the population size (e.g., 100): ", int, min_val=10) + genes = get_validated_input("Enter the number of genes per individual (e.g., 50): ", int, min_val=1) + generations = get_validated_input("Enter the number of generations to run (e.g., 100): ", int, min_val=1) + cross_rate = get_validated_input("Enter the crossover rate (e.g., 0.8): ", float, min_val=0.0, max_val=1.0) + mut_rate = get_validated_input("Enter the mutation rate (e.g., 0.01): ", float, min_val=0.0, max_val=1.0) + + # Create and run the solver with user-defined parameters + ga_solver = GeneticAlgorithm( + fitness_func=one_max_fitness, + population_size=pop_size, + num_genes=genes, + crossover_rate=cross_rate, + mutation_rate=mut_rate + ) + + best_solution, best_score = ga_solver.solve(generations) + + print("\n--- šŸ“Š Results ---") + print(f"Best solution found: {best_solution}") + print(f"Fitness score: {best_score} (out of a maximum possible {genes})") \ No newline at end of file diff --git a/tests/test_genetic_algorithm.py b/tests/test_genetic_algorithm.py new file mode 100644 index 00000000..395cc83d --- /dev/null +++ b/tests/test_genetic_algorithm.py @@ -0,0 +1,36 @@ +# tests/test_genetic_algorithm.py + +import unittest +# Make sure to import your GeneticAlgorithm class +from job_shop_lib.genetic_algorithm import GeneticAlgorithm + +# A simple fitness function for testing purposes +def dummy_fitness(individual): + return sum(individual) + +class TestGeneticAlgorithm(unittest.TestCase): + + def setUp(self): + """Set up a GA instance before each test.""" + self.ga_solver = GeneticAlgorithm( + fitness_func=dummy_fitness, + population_size=10, + num_genes=8 + ) + + def test_initialization(self): + """Test if the population is initialized correctly.""" + self.assertEqual(len(self.ga_solver.population), 10) + self.assertEqual(len(self.ga_solver.population[0]), 8) + + def test_solve_returns_valid_output(self): + """Test if the solve method runs and returns the correct format.""" + num_generations = 5 + best_solution, best_score = self.ga_solver.solve(num_generations) + + # Check if the output has the correct length and the score is a number + self.assertEqual(len(best_solution), 8) + self.assertIsInstance(best_score, (int, float)) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file