NicolleLouis

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

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

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 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 fitness(individual, item_set):
    Knapsack_Capacity = round(total_weight_of_item_set(item_set) / 2)
    result = 0
    if (weight_of_individual(individual, item_set) <= Knapsack_Capacity):
        result = 2 * value_of_individual(individual, item_set) - weight_of_individual(individual, item_set)
    return result

NicolleLouis

def select_breeders(population_sorted, size_of_population):
    result = []
    best_individuals = size_of_population / 5
    lucky_few = size_of_population / 5
    for i in range(best_individuals):
        result.append(population_sorted[i])
    for i in range(lucky_few):
        result.append(random.choice(population_sorted))
    random.shuffle(result)
    return result

NicolleLouis

def generate_one_individual(item_set):
    individual = []
    for i in range(len(item_set)):
        if (100 * random.random() < 50):
            individual.append(True)
        else:
            individual.append(False)
    return individual

NicolleLouis

def generate_one_item(Max_weight, Max_value):
    item = []
    item.append(round(Max_weight * random.random()))
    item.append(round(Max_value * random.random()))
    return item


def generate_all_items(Number_of_item, Max_weight, Max_value):
    list_item = []
    for i in range(Number_of_item):

NicolleLouis

#!/usr/bin/python3.5
# -*-coding:Utf-8 -*

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

temps1 = time.time()

NicolleLouis

import random

def mutateWord(word):
	index_modification = int(random.random() * len(word))
	if (index_modification == 0):
		word = chr(97 + int(26 * random.random())) + word[1:]
	else:
		word = word[:index_modification] + chr(97 + int(26 * random.random())) + word[index_modification+1:]
	return word