Multi-Start Genetic Algorithm (Python Code)

import numpy as np
import random
 
def objective_function(pop):
    fitness = np.zeros(pop.shape[0])
    for i in range(pop.shape[0]):
        x=pop[i]
        fitness[i] = 10e6-(-(x[1]+47)*np.sin(np.sqrt(abs(x[0]/2+(x[1]+47))))-x[0]*np.sin(np.sqrt(abs(x[0]-(x[1]+47)))))
    return fitness
 
def selection(pop, fitness, pop_size):
    next_generation = np.zeros((pop_size, pop.shape[1]))
    elite = np.argmax(fitness)
    next_generation[0] = pop[elite]  # keep the best
    fitness = np.delete(fitness,elite)
    pop = np.delete(pop,elite,axis=0)
    P = [f/sum(fitness) for f in fitness]  # selection probability
    index = list(range(pop.shape[0]))
    index_selected = np.random.choice(index, size=pop_size-1, replace=False, p=P)
    s = 0
    for j in range(pop_size-1):
        next_generation[j+1] = pop[index_selected[s]]
        s +=1
    return next_generation
 
def crossover(pop, crossover_rate):
    offspring = np.zeros((crossover_rate, pop.shape[1]))
    for i in range(int(crossover_rate/2)):
        r1=random.randint(0, pop.shape[0]-1)
        r2 = random.randint(0, pop.shape[0]-1)
        while r1 == r2:
            r1 = random.randint(0, pop.shape[0]-1)
            r2 = random.randint(0, pop.shape[0]-1)
        cutting_point = random.randint(1, pop.shape[1] - 1)
        offspring[2*i, 0:cutting_point] = pop[r1, 0:cutting_point]
        offspring[2*i, cutting_point:] = pop[r2, cutting_point:]
        offspring[2*i+1, 0:cutting_point] = pop[r2, 0:cutting_point]
        offspring[2*i+1, cutting_point:] = pop[r1, cutting_point:]
    return offspring
 
def mutation(pop, mutation_rate):
    offspring = np.zeros((mutation_rate, pop.shape[1]))
    for i in range(int(mutation_rate/2)):
        r1=random.randint(0, pop.shape[0]-1)
        r2 = random.randint(0, pop.shape[0]-1)
        while r1 == r2:
            r1 = random.randint(0, pop.shape[0]-1)
            r2 = random.randint(0, pop.shape[0]-1)
        cutting_point = random.randint(0, pop.shape[1]-1)
        offspring[2*i] = pop[r1]
        offspring[2*i,cutting_point] = pop[r2,cutting_point]
        offspring[2*i+1] = pop[r2]
        offspring[2*i+1, cutting_point] = pop[r1, cutting_point]
    return offspring
 
def local_search(pop, fitness, lower_bounds, upper_bounds, step_size, rate):
    index = np.argmax(fitness)
    offspring = np.zeros((rate*2*pop.shape[1], pop.shape[1]))
    for r in range(rate):
        offspring1 = np.zeros((pop.shape[1], pop.shape[1]))
        for i in range(int(pop.shape[1])):
            offspring1[i] = pop[index]
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Sorry! This is only a half of the code.