NicolleLouis

def mutate_one_individual(individual, mutationRate):
    for geneIndex in range(len(individual)):
        if (100 * random.random() < mutationRate):
            individual[geneIndex] = not individual[geneIndex]
    return individual


def mutate_population(population, mutationRate):
    for individual in population:
        individual = mutate_one_individual(individual, mutationRate)

NicolleLouis

def mutate_one_individual(individual):
  i = int(len(individual) * random.random())
  individual[i] = not individual[i]
  return individual

def mutate_population(population, mutationRate):
  for i in population:
    if (100 * random.random() < mutationRate):
      i = mutate_one_individual(i)
  return (population)

NicolleLouis

def evolve_several_generation_with_limited_time(item_set, size_of_population, number_of_child, time_limit,
                                                mutationRate):
    temps_init = time.time()
    value0 = 0
    result = []
    population = generate_first_population(item_set, size_of_population)
    value0 = max(value0, value(get_best_individual_in_population(population), item_set))
    result.append(value0)
    while (time.time() - temps_init < time_limit):
        population_sorted = sort_population_by_fitness(population, item_set)

NicolleLouis

import random
import operator
import time
import matplotlib.pyplot as plt
import functools