Create function_optimization.py

genetic_algorithm/genetic_algorithm_optimization.py

@@ -0,0 +1,125 @@
+import numpy as np
+
+
+class GeneticAlgorithmOptimizer:
+    def __init__(
+        self,
+        func,
+        bounds,
+        population_size=100,
+        generations=500,
+        crossover_prob=0.9,
+        mutation_prob=0.01,
+    ):
+        self.func = func
+        self.bounds = np.array(bounds)
+        self.population_size = population_size
+        self.generations = generations
+        self.crossover_prob = crossover_prob
+        self.mutation_prob = mutation_prob
+        self.num_variables = len(bounds)
+
+        # Initialize the random number generator
+        self.rng = np.random.default_rng()
+
+    def initialize_population(self):
+        """
+        Initialize a population of random solutions within the bounds.
+        """
+        return self.rng.uniform(
+            low=self.bounds[:, 0],
+            high=self.bounds[:, 1],
+            size=(self.population_size, self.num_variables),
+        )
+
+    def fitness(self, individual):
+        """
+        Evaluate the fitness of an individual.
+        In minimization problems, we aim to minimize the function value.
+        """
+        return self.func(*individual)
+
+    def select_parents(self, population, fitness_scores):
+        """
+        Select parents using tournament selection.
+        """
+        selected_indices = self.rng.choice(
+            range(self.population_size), size=2, replace=False
+        )
+        return population[selected_indices[np.argmin(fitness_scores[selected_indices])]]
+
+    def crossover(self, parent1, parent2):
+        """
+        Perform one-point crossover to create offspring.
+        Skip crossover for single-variable functions.
+        """
+        if self.num_variables == 1:
+            return parent1, parent2  # No crossover needed for single-variable functions
+
+        if self.rng.random() < self.crossover_prob:
+            point = self.rng.integers(1, self.num_variables)
+            child1 = np.concatenate((parent1[:point], parent2[point:]))
+            child2 = np.concatenate((parent2[:point], parent1[point:]))
+            return child1, child2
+        return parent1, parent2
+
+    def mutate(self, individual):
+        """
+        Apply mutation to an individual with a given mutation probability.
+        """
+        if self.rng.random() < self.mutation_prob:
+            index = self.rng.integers(0, self.num_variables)
+            individual[index] = self.rng.uniform(
+                self.bounds[index, 0], self.bounds[index, 1]
+            )
+        return individual
+
+    def evolve(self):
+        """
+        Run the genetic algorithm for a number of generations.
+        """
+        population = self.initialize_population()
+        best_solution = None
+        best_fitness = float("inf")
+
+        for gen in range(self.generations):
+            fitness_scores = np.array(
+                [self.fitness(individual) for individual in population]
+            )
+
+            new_population = []
+            for _ in range(self.population_size // 2):
+                parent1 = self.select_parents(population, fitness_scores)
+                parent2 = self.select_parents(population, fitness_scores)
+                child1, child2 = self.crossover(parent1, parent2)
+                child1 = self.mutate(child1)
+                child2 = self.mutate(child2)
+                new_population.extend([child1, child2])
+
+            population = np.array(new_population)
+
+            # Track the best solution
+            min_fitness_index = np.argmin(fitness_scores)
+            if fitness_scores[min_fitness_index] < best_fitness:
+                best_fitness = fitness_scores[min_fitness_index]
+                best_solution = population[min_fitness_index]
+
+            print(f"Generation {gen + 1}, Best Fitness: {best_fitness}")
+
+        return best_solution, best_fitness
+
+
+if __name__ == "__main__":
+    # Define the function to optimize
+    def func(x, y):
+        return x**2 + y**2  # Example: Minimizing x^2 + y^2
+
+    # Define the bounds for each variable
+    bounds = [(-10, 10), (-10, 10)]
+
+    # Initialize and run the optimizer
+    optimizer = GeneticAlgorithmOptimizer(func=func, bounds=bounds)
+    best_solution, best_fitness = optimizer.evolve()
+
+    print("Best Solution:", best_solution)
+    print("Best Fitness:", best_fitness)