"""Single-objective optimization example using pymoo. This script demonstrates basic single-objective optimizationusing the Genetic Algorithm on the Sphere function.""" from pymoo.algorithms.soo.nonconvex.ga import GAfrom pymoo.problems import get_problemfrom pymoo.optimize import minimizefrom pymoo.operators.crossover.sbx import SBXfrom pymoo.operators.mutation.pm import PMfrom pymoo.operators.sampling.rnd import FloatRandomSamplingfrom pymoo.termination import get_terminationimport numpy as np  def run_single_objective_optimization():    """Run single-objective optimization example."""     # Define the problem - Sphere function (sum of squares)    problem = get_problem("sphere", n_var=10)     # Configure the algorithm    algorithm = GA(        pop_size=100,        sampling=FloatRandomSampling(),        crossover=SBX(prob=0.9, eta=15),        mutation=PM(eta=20),        eliminate_duplicates=True    )     # Define termination criteria    termination = get_termination("n_gen", 100)     # Run optimization    result = minimize(        problem,        algorithm,        termination,        seed=1,        verbose=True    )     # Print results    print("\n" + "="*60)    print("OPTIMIZATION RESULTS")    print("="*60)    print(f"Best solution: {result.X}")    print(f"Best objective value: {result.F[0]:.6f}")    print(f"Number of generations: {result.algorithm.n_gen}")    print(f"Number of function evaluations: {result.algorithm.evaluator.n_eval}")    print("="*60)     return result  if __name__ == "__main__":    result = run_single_objective_optimization()