Python Error: 'int' object has no attribute '__getitem__'

I'm writing a genetic algorithm in Python to find the least of some mathematical functions. The problem is that when I calculate the fitness of a function and save the result in the fitness list, it presents the following error:

Traceback (most recent call last):
  File "ag_GP.py", line 95, in <module>
    run()
  File "ag_GP.py", line 84, in run
    ax = fitness_func()
  File "ag_GP.py", line 29, in fitness_func
    fitness_value = ( 1+(oPop[i][0]+oPop[i][1]+1)**2 * (19-14*oPop[i][0]+3*oPop[i][0]**2-14*oPop[i][1]+6*oPop[i][0]*oPop[i][1]+3*oPop[i][1]**2) ) * ( 30+(2*oPop[i][0]-3*oPop[i][1])**2 * (18-32*oPop[i][0]+12*oPop[i][0]**2+48*oPop[i][1]-36*oPop[i][0]*oPop[i][1]+27*oPop[i][1]**2) ) 

TypeError: 'int' object has no attribute '__getitem__'

Follow the program code as well:

import random
import math

BEST_FITNESS    = 3
CHRM_SIZE       = 2
POP_SIZE        = 5
MAX_GENERATIONS = 5
MUTATION_RATE   = 0.8

MIN,MAX = -2,2

oPop = []
nPop = []
fitness = []
sucess_rate = 0
fitness_sum = 0

def init_population():
    for i in range(0,POP_SIZE):
        chromosome = []
        for j in range(CHRM_SIZE):
            chromosome.append(random.randint(MIN,MAX))
        oPop.append(chromosome)

def fitness_func():
    global fitness_sum, fitness

    for i in range(0,len(oPop)):
        fitness_value = ( 1+(oPop[i][0]+oPop[i][1]+1)**2 * (19-14*oPop[i][0]+3*oPop[i][0]**2-14*oPop[i][1]+6*oPop[i][0]*oPop[i][1]+3*oPop[i][1]**2) ) * ( 30+(2*oPop[i][0]-3*oPop[i][1])**2 * (18-32*oPop[i][0]+12*oPop[i][0]**2+48*oPop[i][1]-36*oPop[i][0]*oPop[i][1]+27*oPop[i][1]**2) ) 
        fitness.append(fitness_value)
        fitness_sum += 1

        if(fitness[i] == BEST_FITNESS):
            return 1
    return 0

def mutation():
    return(random.randint(MIN,MAX))

def crossover(parent1, parent2):
    return (parent1[:CHRM_SIZE/2]+parent2[CHRM_SIZE/2:])

def stochastic_selection():
    return (random.randint(0, POP_SIZE-1))

def best_individual():
    ax = 0
    best_fitness = fitness[0]

    for i in range(0,len(fitness)):
        if(abs(fitness[i] - BEST_FITNESS) < best_fitness):
            best_fitness = fitness[i]
            ax = i
    return ax


def next_generation():
    a = best_individual()
    nPop.append(a)

    for i in range(1,len(oPop)):
        parent1,parent2 = stochastic_selection(), stochastic_selection()
        nPop.append(crossover(oPop[parent1],oPop[parent2]))

        mut_rate = random.randint(0,POP_SIZE)

        if(mut_rate <= MUTATION_RATE*100):
            j = random.randint(0,CHRM_SIZE-1)
            nPop[i][j] = mutation()

    for i in range(0,POP_SIZE):
        oPop[i] = nPop[i]

def reset():
    global oPop, nPop, fitness
    for i in range(len(oPop)):
        oPop, nPop, fitness = [],[],[]

def run():
    generation = 0

    init_population()
    while (1):
        ax = fitness_func()
        if(ax == 1 or generation >= MAX_GENERATIONS):
            return generation

        next_generation()
        generation += 1

    return generation

if __name__ == '__main__':
    for i in range(0,100):
        run()
        if(abs(fitness[best_individual()] - BEST_FITNESS) < 0.01):
            sucess_rate += 1
        reset()

    print(fitness_sum)
    print(sucess_rate)