deap

import random from deap import creator, base, tools, algorithms # 1. Define problem: Maximize sum of bits in a binary string (OneMax problem) # Create a FitnessMax class, higher values are better creator.create("FitnessMax", base.Fitness, weights=(1.0,)) # Create an Individual class, which is a list and has a fitness attribute creator.create("Individual", list, fitness=creator.FitnessMax) # 2. Initialize Toolbox toolbox = base.Toolbox() # Register function to generate random boolean attributes (0 or 1) toolbox.register("attr_bool", random.randint, 0, 1) # Register function to create an individual: 100 random booleans toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) # Register function to create a population: a list of individuals toolbox.register("population", tools.initRepeat, list, toolbox.individual) # 3. Define Evaluation Function def evalOneMax(individual): return sum(individual), # The comma is crucial: returns a tuple toolbox.register("evaluate", evalOneMax) # 4. Define Genetic Operators toolbox.register("mate", tools.cxTwoPoint) # Two-point crossover toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) # Flip bit mutation with 5% probability toolbox.register("select", tools.selTournament, tournsize=3) # Tournament selection with size 3 # 5. Run the Evolutionary Algorithm def main(): random.seed(42) # For reproducibility population = toolbox.population(n=300) # Number of generations, crossover probability, mutation probability NGEN, CXPB, MUTPB = 40, 0.7, 0.2 # The main evolutionary loop print(f"Start of evolution: Population size {len(population)}") population, logbook = algorithms.eaSimple(population, toolbox, cxpb=CXPB, mutpb=MUTPB, ngen=NGEN, verbose=False) # Get the best individual(s) from the final population best_ind = tools.selBest(population, 1)[0] print(f"Best individual: {best_ind}, Fitness: {best_ind.fitness.values[0]}") if __name__ == "__main__": main()