How to fix premature convergence in simple GA (Python)?

Yesterday i started exploring the genetic algorithms, and when i ended up with some basic theory, i tried to write simple GA on Python, that solves Diophantine equation. I'm new to Python and GAs, so please, don't judge my code strictly.

Problem

I cant get any result due to premature convergence (there is some no-return point (n-population), population[n] == population[n+i], where i is any integer. even the random mutatuion element cant change this, the generation is degradating very quickly)

GA is using crossover to breed, and weighted choice of parents.

• Q1: Is there any design mistakes in my code (below)?
• Q1.2: Do i need to add elitism?
• Q1.3: Do i need to change breed logic?
• Q2: Is there realy needed deep copy?

Code:

# -*- coding: utf-8 -*-
from random import randint
from copy import deepcopy
from math import floor
import random

class Organism:
    #initiate
    def __init__(self, alleles, fitness, likelihood):
        self.alleles = alleles
        self.fitness = fitness
        self.likelihood = likelihood
        self.result = 0
    def __unicode__(self):
        return '%s [%s - %s]' % (self.alleles, self.fitness, self.likelihood)

class  CDiophantine:
    def __init__(self, coefficients,  result):
        self.coefficients = coefficients
        self.result = result

    maxPopulation = 40
    organisms = []
    def GetGene (self,i):
        return self.organisms[i]

    def OrganismFitness (self,gene):
        gene.result = 0
        for i in range (0, len(self.coefficients)):
            gene.result += self.coefficients[i]*gene.alleles[i]
        gene.fitness = abs(gene.result - self.result)
        return gene.fitness

    def Fitness (self):
        for organism in self.organisms:
            organism.fitness = self.OrganismFitness(organism)
            if organi