a280_n279_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb67df54",
   "metadata": {},
   "source": [
    "# Group F Submission Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec5c672f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import pickle\n",
    "import ast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ebbec6",
   "metadata": {},
   "source": [
    "Parameters kept this way for testing purposes. For optimal results parameter configurations are as follow;\n",
    "\n",
    "| Problem         | Runtime | N    | Ne   | Nm   | p    | a    | w    | Q          |\n",
    "|-----------------|---------|------|------|------|------|------|------|------------|\n",
    "| a280-n279       | 18000   | 500  | 0.3  | 0.2  | 0.8  | 0.3  | 50   | 25936      |\n",
    "| a280-n1395      | 18000   | 1000 | 0.6  | 0.1  | 0.8  | 0.3  | 50   | 637010     |\n",
    "| a280-n2790      | 18000   | 1000 | 0.6  | 0    | 0.5  | 0.3  | 50   | 1262022    |\n",
    "|                 |         |      |      |      |      |      |      |            |\n",
    "| fnl4460-n4460   | 10800   | 1000 | 0.6  | 0    | 0.5  | 0.2  | 50   | 387150     |\n",
    "| fnl4460-n22300  | 10800   | 1000 | 0.6  | 0    | 0.5  | 0.1  | 50   | 10182055   |\n",
    "| fnl4460-n44600  | 10800   | 1000 | 0.6  | 0    | 0.5  | 0.3  | 100  | 20244159   |\n",
    "|                 |         |      |      |      |      |      |      |            |\n",
    "| pla33810-n33809 | 10800   | 1000 | 0.6  | 0    | 0.6  | 0.1  | 50   | 1539600049 |\n",
    "| pla33810-n169045| 5400    | 1000 | 0.3  | 0    | 0.8  | 0.3  | 1    | 77184794   |\n",
    "| pla33810-n338090| 5400    | 1000 | 0.6  | 0    | 0.7  | 0.3  | 100  | 153960049  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "604c80ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 500 #Population size\n",
    "Q = 25936 #Capaciy of knap sack\n",
    "Ne = int(0.3*(N)) #Number of elites\n",
    "Nm = int(0.2*N) #Number of Mutants\n",
    "prob = 0.8 #Probability of elites in bias crossovers\n",
    "alpha = 0.3 #fraction of sample to generate\n",
    "freq = 50 #Frequncy of local search\n",
    "epoch = 18000 #Termination condition  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b268355",
   "metadata": {},
   "source": [
    "To test that our algorithm runs, we would use the test data but to see it's performance on the other datasets for this coursework, use the appropriate filenames in the cell below\n",
    "\n",
    "| Name            | dataset_fl       | distance_fl                        | TSP_fl                                | KP_fl                              |\n",
    "|-----------------|------------------|------------------------------------|---------------------------------------|------------------------------------|\n",
    "| Test data       | a.csv            | distance_matrix                     | TSP_solutions                          | KP_Solutions                       |\n",
    "| a280-n279       | a280-n279.csv    | distance_matrix_a280-n2790.pkl      | TSP_solutions_a280-n2790.txt          | a280-n279 KP_Solutions.txt         |\n",
    "| a280-n1395      | a280-n1395.csv   | distance_matrix_a280-n2790.pkl      | TSP_solutions_a280-n2790.txt          | a280-n1395 KP_Solutions.txt        |\n",
    "| a280-n2790      | a280-n2790.csv   | distance_matrix_a280-n2790.pkl      | TSP_solutions_a280-n2790.txt          | a280-n2790 KP_Solutions.txt        |\n",
    "| fnl4461-n4460   | fnl4461-n4460.csv| distance_matrix_fnl4461-n4460.pkl   | TSP_solutions_fnl4461-n4460.txt       | fnl4461-n4460 KP_Solutions.txt     |\n",
    "| fnl4461-n22300  | fnl4461-n22300.csv| distance_matrix_fnl4461-n4460.pkl  | TSP_solutions_fnl4461-n4460.txt       | fnl4461-n22300 KP_Solutions.txt    |\n",
    "| fnl4461-n44600  | fnl4461-n44600.csv| distance_matrix_fnl4461-n4460.pkl  | TSP_solutions_fnl4461-n4460.txt       | fnl4461-n44600 KP_Solutions.txt    |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8a53022",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_fl = './datasets/a280-n279.csv' #Filename of the input dataset\n",
    "TSP_fl = './TSP Solutions/TSP_solutions_a280-n2790.txt' # TSP solutions file\n",
    "KP_fl = './KP_Solutions/a280-n279 KP_Solutions.txt' # KP solutions file\n",
    "distance_fl = './TSP Solutions/distance_matrix_a280-n2790.pkl' #Distance matix file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5b15fb7",
   "metadata": {},
   "source": [
    "## Read the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d11ac156",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write the code to read the data from csv files into df\n",
    "df = pd.read_csv(dataset_fl)\n",
    "# Transfer weights and profits into float numbers\n",
    "for i in range(len(df['weights'])):\n",
    "    w_list = df['weights'][i]\n",
    "    str_list = w_list[1:-1].split(' ')\n",
    "    new_w_list = [float(x) for x in str_list if x.strip()]\n",
    "    df.at[i,'weights'] = new_w_list\n",
    "for i in range(len(df['profit'])):\n",
    "    p_list = df['profit'][i]\n",
    "    str_list = p_list[1:-1].split(' ')\n",
    "    new_p_list = [float(x) for x in str_list if x.strip()]\n",
    "    df.at[i,'profit'] = new_p_list\n",
    "\n",
    "\n",
    "## distance_matrix\n",
    "with open(distance_fl,'rb') as file:\n",
    "    distance_matrix = pickle.load(file)\n",
    "\n",
    "TSP_solution = np.loadtxt(TSP_fl)\n",
    "\n",
    "\n",
    "file_path = KP_fl\n",
    "\n",
    "# Read the contents of the file\n",
    "with open(file_path, 'r') as file:\n",
    "    lines = file.readlines()\n",
    "# Parse the contents to get the packing orders as a list of lists\n",
    "KP_solution = [np.array([int(val) for val in ast.literal_eval(line.strip())]) for line in lines]\n",
    "KP_solution = np.array(KP_solution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c568eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weights\n",
    "weights_new = (df['weights'].tolist())\n",
    "weights = [item[:] for item in weights_new if item]\n",
    "weights = np.array(weights).flatten()\n",
    "\n",
    "# profits\n",
    "profit_new = (df['profit'].tolist())\n",
    "profit = [item[:] for item in profit_new if item]\n",
    "profit = np.array(profit).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c60cd833",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write the fitness function\n",
    "def get_fitness(solution, df, distances, Q, v_min=0.1, v_max=1):\n",
    "    # Cost\n",
    "    TSP_solution = solution['TSP']\n",
    "    KP_solution = solution['KP']\n",
    "    time = 0\n",
    "    weights = df['weights']\n",
    "\n",
    "    # calculate weight for each city\n",
    "    KP_list = []\n",
    "    idx = 0\n",
    "    for i in range(len(weights)):\n",
    "        w = weights[i]\n",
    "        KP = KP_solution[idx:idx+len(w)]\n",
    "        idx += len(w)\n",
    "        KP_list.append(KP)\n",
    "\n",
    "    # calculate speed\n",
    "    speed = [(v_max - (np.sum(x) / Q) * (v_max - v_min)) if (v_max - (np.sum(x) / Q) * (v_max - v_min)) > 0.1 else 0.1 for x, weight in zip(KP_list, weights)]\n",
    "    # calculate time\n",
    "    n = len(TSP_solution)\n",
    "    #print(n)\n",
    "    for i in range(n-1):\n",
    "        #print(distances[int(TSP_solution[i]),int(TSP_solution[i+1])])\n",
    "        #print(distances[TSP_solution[i]][TSP_solution[i+1]])\n",
    "        time += distances[int(TSP_solution[i]),int(TSP_solution[i+1])]/speed[i]\n",
    "    time += distances[int(TSP_solution[n-1]),int(TSP_solution[0])]/speed[n-1]\n",
    "\n",
    "    # Value\n",
    "    profits = df['profit']\n",
    "    v_list = []\n",
    "    for w in profits:\n",
    "        v_list.extend(w)\n",
    "    item_value = pd.DataFrame({'z':KP_solution,'profit':v_list})\n",
    "    item_value['value'] = item_value['z']*item_value['profit']\n",
    "    value = -1 * np.sum(item_value['value'])\n",
    "\n",
    "    # combine 2_opt\n",
    "    solution['cost'] = np.array([time, value])\n",
    "    return solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e17e91d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculates the value/wieght ratio and stores in an array\n",
    "def calculate_ratio(KP,values,weights):\n",
    "    value_weight_ratio = np.array(values) / np.array(weights)\n",
    "    value_weight_ratio = value_weight_ratio.flatten()\n",
    "    result = [a * b for a, b in zip(KP, value_weight_ratio)]\n",
    "    #print(result)\n",
    "    return result\n",
    "\n",
    "def find_highest_value_index(arr):\n",
    "    max_value = max(arr)\n",
    "    return arr.index(max_value)\n",
    "\n",
    "\"\"\"#Fitness function, currently used as random\n",
    "def fitness(pi,z):\n",
    "    '''THIS NEEDS TO BE REMOVED'''\n",
    "    return random.randint(1, 100)\"\"\"\n",
    "\n",
    "'''\n",
    "TSP_solutions = [[1,2,3,4],[1, 4, 3, 2], [1,3, 2, 4]]  # These are the TSP solutions\n",
    "KP_solutions = [[1,0,1,1,0,1,0,0,1,1,0,0,1,0,1],[0,1,1,0,1,0,1,1,0,0,1,0,1,1,0]]\n",
    "weights = [10,15,20,25,30,20,15,25,15,30,36, 21, 48, 14, 31]\n",
    "values = [100,250,50,200,150,215, 178, 292, 134, 251,189, 204, 123, 278, 156]\n",
    "'''\n",
    "\n",
    "def initial_population(TSP_solutions,KP_solutions,N, weights,values, distances, Q, alpha=0.4):\n",
    "    S = []\n",
    "    n_cities = len(TSP_solution[0])\n",
    "    n_bagsize = len(KP_solutions[0])\n",
    "    #print(TSP_solutions)\n",
    "    for tsp_sols in range(0,len(TSP_solutions)):\n",
    "        #get the first TSP solution and reverse it\n",
    "        pi1 = TSP_solutions[tsp_sols]\n",
    "        pi2 = pi1[::-1]\n",
    "        pi2 = np.concatenate([pi2[-1:], pi2[:-1]])\n",
    "        for kp_sols in range(0,len(KP_solutions)):\n",
    "            #get the first KP solution and create an empty one as well\n",
    "            z1 = KP_solutions[kp_sols]\n",
    "            z2= [0] * (len(z1))\n",
    "            z2 = np.array(z2)\n",
    "\n",
    "            #create two individuals and add it to S\n",
    "            enc1 = {'TSP': pi1, 'KP':z2}\n",
    "            enc2 = {'TSP': pi2, 'KP':z2}\n",
    "            #S.append(enc1)\n",
    "            #S.append(enc2)\n",
    "            #find the indecis with value 1 in kp solution\n",
    "\n",
    "            arr = calculate_ratio(z1,values,weights)\n",
    "            #print(arr)\n",
    "\n",
    "            length = sum(1 for value in arr if value > 0)\n",
    "            #print(length)\n",
    "\n",
    "            i=0\n",
    "            while(i<length):\n",
    "                #find the index with highest ratio and remove from arr\n",
    "                max_index = find_highest_value_index(arr)\n",
    "                #print(max_index)\n",
    "                arr[max_index] = 0.0\n",
    "                #print(arr)\n",
    "\n",
    "                #add the highest ratio into Z2\n",
    "                z2[max_index] = 1\n",
    "                sol1 = {'TSP': pi1, 'KP':z2}\n",
    "                sol2 = {'TSP': pi2, 'KP':z2}\n",
    "                fitness1 = get_fitness(sol1,df, distances, Q)\n",
    "                fitness2 = get_fitness(sol2,df, distances, Q)\n",
    "\n",
    "                is_dominant = dominates(fitness1, fitness2)\n",
    "\n",
    "                \n",
    "                del fitness1['cost'], fitness2['cost']\n",
    "\n",
    "\n",
    "                #find the elite solution and append in S\n",
    "                if(is_dominant):\n",
    "                    S.append(fitness1)\n",
    "                else:\n",
    "                    S.append(fitness2)\n",
    "\n",
    "                i+=1\n",
    "\n",
    "    #randomly generate alpha\n",
    "    #alpha = random.uniform(0, 1) # 0.4\n",
    "    #A = random.sample(S, int(alpha * N)) # 40\n",
    "    A = np.random.choice(S, int(alpha*N))\n",
    "    B = mutant_generator(N-int(alpha*N), n_cities, n_bagsize, False)\n",
    "    \n",
    "    P = np.append(A, B)\n",
    "\n",
    "    return P #array of dictioaries with\n",
    "\n",
    "## Kanny's Mutation Generator\n",
    "'''def mutation(alpha,n,TSP_solutions,KP_solutions,):\n",
    "    B = []\n",
    "    for _ in range(int((1-alpha)*n)):\n",
    "        tsp_component = random.choice(TSP_solutions)\n",
    "        kp_component = random.choice(KP_solutions)\n",
    "        flag = {'TSP': tsp_component, 'KP':np.array(kp_component)}\n",
    "        B.append(flag)\n",
    "    return B\n",
    "'''\n",
    "\n",
    "## Esosa's Mutant Generator\n",
    "def mutant_generator(Nm, n_cities, n_knapsack, encode=False):\n",
    "      '''Randomly generates Ne number of solutions where Nm is the number of mutants'''\n",
    "      mut_sols = [None]*Nm\n",
    "      for i in range(Nm):\n",
    "            tour_sample_plan = np.arange(1, n_cities)\n",
    "            np.random.shuffle(tour_sample_plan) # randomly shuffles inplace\n",
    "            rand_knapsack = np.random.randint(2, size=n_knapsack) # randomly generates an array of 1s and 0s for size n_knapsack\n",
    "            mut_sol = {'TSP': np.append(0,tour_sample_plan),\n",
    "                       'KP': rand_knapsack}\n",
    "            \n",
    "            # encode phenotype of encode == True\n",
    "            if encode:\n",
    "                  mut_sol = encode_phenotype_to_genotype(mut_sol)\n",
    "\n",
    "            # add mutant to list\n",
    "            mut_sols[i] = mut_sol\n",
    "\n",
    "      return np.array(mut_sols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15c09636",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoder\n",
    "def encode_phenotype_to_genotype(solution):\n",
    "    # Assuming the first city is the starting point and is not included in the genotype\n",
    "    #print(solution)\n",
    "    tour_phenotype = solution['TSP'][1:]  # Exclude the first city for TSP genotype\n",
    "\n",
    "    packing_phenotype = solution['KP']\n",
    "\n",
    "    # Generate random genotype values for TSP\n",
    "    tour_genotype_values = sorted([round(random.random(), 8) for _ in range(len(tour_phenotype))])\n",
    "    # Create a mapping of phenotype to genotype values based on the sorted order\n",
    "    #tour_genotype = [tour_genotype_values[tour_phenotype.index(city)] for city in sorted(tour_phenotype)]\n",
    "    tour_genotype = [tour_genotype_values[list(tour_phenotype).index(city)] for city in sorted(tour_phenotype)]\n",
    "\n",
    "    \n",
    "\n",
    "    # Encode the packing plan: Assign 0.6 if item is picked (1), random < 0.5 otherwise\n",
    "    packing_genotype = [round(random.uniform(0.5, 1.0), 8) if item == 1 else round(random.uniform(0, 0.4999), 8) for item in packing_phenotype]\n",
    "    \n",
    "\n",
    "    return {'TSP': np.array(tour_genotype), 'KP': np.array(packing_genotype)}\n",
    "\n",
    "# # Example usage\n",
    "# solution = {\n",
    "#     'TSP': [0, 2, 1, 3], \n",
    "#     'KP': [1, 0, 1, 0, 0, 0]  \n",
    "# }\n",
    "\n",
    "# # Encode the phenotype back into a genotype\n",
    "# genotype = encode_phenotype_to_genotype(solution)\n",
    "\n",
    "# print(genotype)\n",
    "\n",
    "#Decoder\n",
    "def decode_genotype_to_phenotype(solution):\n",
    "    tour_genotype = solution['TSP']\n",
    "    packing_genotype = solution['KP']\n",
    "\n",
    "    cities = np.arange(1, len(tour_genotype)+1)\n",
    "\n",
    "    # get argnindx that sort tour\n",
    "    argidx = np.argsort(tour_genotype)\n",
    "    tour_order = np.append([0], cities[argidx])\n",
    "\n",
    "\n",
    "    # Decoding the tour\n",
    "    #tour_order = [0] + sorted(range(1, len(tour_genotype) + 1), key=lambda i: tour_genotype[i-1])\n",
    "\n",
    "    # Decoding the packing plan\n",
    "    packing_plan = [1 if gen > 0.5 else 0 for gen in packing_genotype]\n",
    "\n",
    "    # Output format is a single long list for the packing plan\n",
    "    return {\n",
    "        'TSP': tour_order,\n",
    "        'KP': np.array(packing_plan)  # Single long list\n",
    "    }\n",
    "# Using the new input provided\n",
    "\n",
    "\n",
    "\n",
    "#Repair operator\n",
    "#Repair operator\n",
    "def repair_operator(phenotype, weights, max_capacity):\n",
    "    \"\"\"\n",
    "    Repairs the phenotype by removing items from the packing plan until it becomes feasible.\n",
    "    Items are removed city by city, starting from the first city visited.\n",
    "\n",
    "    :param phenotype: The phenotype representation of the packing plan and tour.\n",
    "    :param weights: List of weights for each item.\n",
    "    :param max_capacity: The maximum capacity of the knapsack.\n",
    "    :return: The repaired phenotype.\n",
    "    \"\"\"\n",
    "    tour_order = phenotype['TSP'][1:]\n",
    "    #packing_plan = phenotype['KP']\n",
    "    fixed_bag = phenotype['KP'].copy()\n",
    "\n",
    "    for i in tour_order:\n",
    "        if np.sum(fixed_bag*weights) > max_capacity:\n",
    "            flip_idx = int(len(fixed_bag)/len(tour_order)) # gets the number of items in each city. They're equal\n",
    "            ### st = i*flip_idx  removed this because it started at the last item in the city instead of the first\n",
    "            st = i*flip_idx - flip_idx # get's the idx which refers to the first item in city i\n",
    "            # flip portion of bag to 0\n",
    "            for j in range(st, st+flip_idx):\n",
    "                #print(j)\n",
    "                fixed_bag[j] = 0\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    return {'TSP': phenotype['TSP'], 'KP': fixed_bag}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e488dfab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NSGA2\n",
    "def dominates(p, q):\n",
    "      \"\"\"Checks if p dominates q\"\"\"\n",
    "      return all(p['cost'] <= q['cost']) and any(p['cost'] < q['cost'])\n",
    "\n",
    "def list_to_DF(population):\n",
    "      '''\n",
    "      Receives the entire list of solutions and converts it to a dataframe for NSGA2 to update rank and crowding distance\n",
    "      Each solution is a dictionary e.g. {'TSP':[], 'KP':[], 'cost': [dist,prof]} and we need to have a 'rank' column which\n",
    "      defines which front the solution belongs to and also a 'crowding_distance' column for crowding distance of each solution\n",
    "      '''\n",
    "      N = len(population)\n",
    "      pop_df = pd.DataFrame({'TSP':[None]*N, 'KP':None, 'cost':None})\n",
    "\n",
    "      for i in range(N):\n",
    "            pop_df['TSP'][i] = population[i]['TSP']\n",
    "            pop_df['KP'][i] = population[i]['KP']\n",
    "            pop_df['cost'][i] = population[i]['cost']\n",
    "\n",
    "      # this gave me issues\n",
    "      #pop_df = pd.DataFrame(population)\n",
    "\n",
    "      # add new columns\n",
    "      pop_df['rank'] = None\n",
    "      pop_df['crowding_distance'] = None\n",
    "\n",
    "      return pop_df\n",
    "\n",
    "def norm_objective(population_df):\n",
    "      '''\n",
    "      Normalizes the objectives before getting non-dom fronts and calculating crowding distance\n",
    "      Refernce: https://link.springer.com/article/10.1007/s40747-017-0061-9\n",
    "      '''\n",
    "\n",
    "      population_df['fitness'] = population_df['cost']\n",
    "      # Create 'traveltime' and 'profit' columns by extracting values from the 'cost' column\n",
    "      population_df[['traveltime', 'profit']] = pd.DataFrame(population_df['cost'].tolist(), index=population_df.index)\n",
    "\n",
    "      # Normalize 'traveltime' and 'profit'\n",
    "      population_df['traveltime'] = (population_df['traveltime'] - population_df['traveltime'].min()) / (population_df['traveltime'].max() - population_df['traveltime'].min())\n",
    "      population_df['profit'] = (population_df['profit'] - population_df['profit'].min()) / (population_df['profit'].max() - population_df['profit'].min())\n",
    "\n",
    "      # Update the 'cost' column with the normalized values\n",
    "      population_df['cost'] = population_df.apply(lambda row: np.array([row['traveltime'], row['profit']]), axis=1)\n",
    "\n",
    "      # Drop the 'traveltime' and 'profit' columns\n",
    "      population_df.drop(['traveltime', 'profit'], axis=1, inplace=True)\n",
    "\n",
    "      return population_df\n",
    "\n",
    "def df_to_list(population):\n",
    "      '''\n",
    "      Receives a list with columns TSP, KP, cost, rank and crowding distance, drops all except TSP and KP and returns a list of dictionaries where\n",
    "      each dictionary represents a row in the dataframe\n",
    "      '''\n",
    "      popl = population[['TSP', 'KP']]\n",
    "\n",
    "      # convert popl to a dictionary of dictionaries\n",
    "      dofd = popl.to_dict(orient='index')\n",
    "      # converts the dofd into a list of dictionaries\n",
    "      lofd = [{**v} for k, v in dofd.items()]\n",
    "\n",
    "      return lofd\n",
    "\n",
    "def non_dominated_sorting(pop_df):\n",
    "      \"\"\"\n",
    "      Perform Non-dominated Sorting on a Population where pop refers to the population dataframe\n",
    "      Each row represents a solution and the columns are TSP, KP, cost, rank and crowding distance\n",
    "      # check out column for the rank of each solution??\n",
    "      \"\"\"\n",
    "      pop_size = len(pop_df) # gets the number of solution\n",
    "\n",
    "      # Initialize Domination Stats\n",
    "      domination_set = [[] for _ in range(pop_size)] # identifies the solutions that each solution dominates. Just stores and empty list for now\n",
    "      dominated_count = [0 for _ in range(pop_size)] # how many solutions dominate each solution\n",
    "\n",
    "      # Initialize Pareto Fronts <non-dominated fronts>\n",
    "      F = [[]]\n",
    "\n",
    "      # Find the first Pareto Front\n",
    "      for i in range(pop_size):\n",
    "            for j in range(i+1, pop_size):\n",
    "                  # Check if i dominates j\n",
    "                  if dominates(pop_df.loc[i], pop_df.loc[j]):\n",
    "                        #if dominates(pop[i], pop[j]):\n",
    "                        domination_set[i].append(j) # if i dom j, then add j to its domination list\n",
    "                        dominated_count[j] += 1 # add 1 to the number of solutions that dominate j\n",
    "\n",
    "                  # Check if j dominates i\n",
    "                  elif dominates(pop_df.loc[j], pop_df.loc[i]):\n",
    "                        #elif dominates(pop[j], pop[i]):\n",
    "                        domination_set[j].append(i)\n",
    "                        dominated_count[i] += 1\n",
    "\n",
    "            # If i is not dominated at all\n",
    "            if dominated_count[i] == 0:\n",
    "                  pop_df.loc[i,'rank'] = 0\n",
    "                  #pop[i]['rank'] = 0\n",
    "                  F[0].append(i) # add to Pareto optimal front\n",
    "\n",
    "      # Pareto Counter\n",
    "      k = 0\n",
    "\n",
    "      while True:\n",
    "\n",
    "            # Initialize the next Pareto front\n",
    "            Q = []\n",
    "\n",
    "            # Find the members of the next Pareto front\n",
    "            for i in F[k]: # for every solution in the curr front\n",
    "                  for j in domination_set[i]: # look at the set of those that dominate it\n",
    "                        dominated_count[j] -= 1 # subtract the members of the previous front from that list\n",
    "                        if dominated_count[j] == 0: # if there's none\n",
    "                              pop_df.loc[j,'rank'] = k + 1\n",
    "                              #pop[j]['rank'] = k + 1\n",
    "                              Q.append(j) # add solutiont to the next front\n",
    "\n",
    "            # Check if the next Pareto front is empty\n",
    "            if not Q:\n",
    "                  break\n",
    "\n",
    "            # Append the next Pareto front\n",
    "            F.append(Q)\n",
    "\n",
    "            # Increment the Pareto counter\n",
    "            k += 1\n",
    "      # return pop_df with ranks updated and also the Fronts, F\n",
    "      return pop_df, F\n",
    "\n",
    "\n",
    "def calc_crowding_distance(pop_df, F):\n",
    "      \"\"\"Calculate the crowding distance for a given population with the Fronts\"\"\"\n",
    "\n",
    "      # Number of Pareto fronts (ranks)\n",
    "      pareto_count = len(F)\n",
    "\n",
    "      # Number of Objective Functions\n",
    "      n_obj = len(pop_df.loc[0, 'cost'])\n",
    "\n",
    "      # Iterate over Pareto fronts\n",
    "      for k in range(pareto_count):\n",
    "            # get all the costs for the elements in the Pareto front. Results in a matrix of size pareto_count x n_obj\n",
    "            costs = np.array([pop_df.loc[i,'cost'] for i in F[k]])\n",
    "            n = len(F[k]) # number of members in k-th front\n",
    "            d = np.zeros((n, n_obj)) # creates a zero matrix of same shape as costs to store crowding distance\n",
    "\n",
    "            # Iterate over objectives, column by column\n",
    "            for j in range(n_obj):\n",
    "                  idx = np.argsort(costs[:, j]) # for the jth obj, get the idx which sorts it. You're basically looking for the boundary solutions\n",
    "                  # assign boundary solutions a distance = infinity\n",
    "                  d[idx[0], j] = np.inf  # first\n",
    "                  d[idx[-1], j] = np.inf # last\n",
    "\n",
    "                  # iterate through the remaining values excluding the bounday solutions\n",
    "                  for i in range(1, n-1):\n",
    "                        # either calculates Euclidean distance or Manhattan to gauge distance\n",
    "                        d[idx[i], j] = costs[idx[i+1], j] - costs[idx[i-1], j]\n",
    "                        d[idx[i], j] /= costs[idx[-1], j] - costs[idx[0], j]\n",
    "\n",
    "            # Calculate Crowding Distance\n",
    "            for i in range(n):\n",
    "                  pop_df.loc[F[k][i], 'crowding_distance'] = sum(d[i, :]) \n",
    "                  #pop_df[F[k][i]]['crowding_distance'] = sum(d[i, :]) # assigns crowding distance based on elements in each front\n",
    "\n",
    "      return pop_df\n",
    "\n",
    "def sort_population(pop):\n",
    "      '''Sorts a population based on rank (in ascending order) and crowding distance (in descending order) to give us our best solutions'''\n",
    "      pop.sort_values(by=['rank', 'crowding_distance'], ascending=[True, False], inplace=True)\n",
    "      # pop = sorted(pop, key = lambda x: (x['rank'], -x['crowding_distance']))\n",
    "\n",
    "      # max_rank = pop[-1]['rank']\n",
    "      # F = []\n",
    "      # for r in range(max_rank + 1):\n",
    "      #       F.append([i for i in range(len(pop)) if pop[i]['rank'] == r])\n",
    "\n",
    "      return pop #, F\n",
    "\n",
    "def elite_nonelite_sepr(pop_df, n_elites):\n",
    "      '''Divides pop_df into a list of elites and non-elites. Where n_elites is the number of elites that we want'''\n",
    "\n",
    "      # since pop_df is already sorted we can just index the first n_elites-th solutions from the df\n",
    "      elites_df = pop_df[:n_elites]\n",
    "      non_elites_df = pop_df[n_elites:]\n",
    "\n",
    "      # convert dfs to lists\n",
    "      elites_L = df_to_list(elites_df)\n",
    "      non_elites_L = df_to_list(non_elites_df)\n",
    "\n",
    "      return elites_L, non_elites_L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "72ed3541",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local Search - Exploitation Phase\n",
    "def local_Search(P_e, W, df, Q, distances, n_of_Ne=0.1):\n",
    "      '''\n",
    "      Performs the local search aspect of the algorithm on a subset of the elite population.\n",
    "         Parameters:\n",
    "            P_e: current elite population\n",
    "            W: Weight of items in each city\n",
    "            df: unknown?? Need to be confirmed from Xiao\n",
    "            Q: Total weight constraint for the knapsack\n",
    "            n_of_Ne: How many of the elite population do we want. Default=0.1\n",
    "         Returns:\n",
    "            P_e with added modified elites\n",
    "\n",
    "      '''\n",
    "      # get subset of elites that we would exploit to make better\n",
    "      n_Pe_hat = round(n_of_Ne*len(P_e))\n",
    "      Pe_hat = np.random.choice(P_e,n_Pe_hat)\n",
    "\n",
    "      for elite in Pe_hat:\n",
    "            # perform 2-opt local search on the TSP aspect\n",
    "            elite = two_opt(elite, df, distances, Q)\n",
    "\n",
    "            # perform bitflip on the KP aspect\n",
    "            elite, P_e = bitflip_exploit(elite, W, Q, P_e, df, distances)\n",
    "\n",
    "      return P_e #only because it contains all the exploited elites\n",
    "\n",
    "def bitflip_exploit(soln, W, Q, P_e, df, distances):\n",
    "      '''\n",
    "      Performs bit-flip given a solution for about min(100,m) moves\n",
    "      where m is the number of items.\n",
    "      Solution format: soln = {'TSP':[], 'KP':[], 'cost': [dist,prof]}\n",
    "      W is the weight and I'm using it's the same length as sol['KP']\n",
    "      '''\n",
    "      route = soln['TSP']\n",
    "      knapsack = soln['KP']\n",
    "      m = len(knapsack)\n",
    "      count = 0\n",
    "\n",
    "      # convert knapsack and W arrs to np.arrays\n",
    "      if ~isinstance(knapsack,np.ndarray):\n",
    "            knapsack = np.array(knapsack)\n",
    "      if ~isinstance(W,np.ndarray):\n",
    "            W = np.array(W)\n",
    "\n",
    "      # create idx values for knapsack\n",
    "      idx_kp = np.arange(len(knapsack))\n",
    "      max_iteration = min(100, m) if m < 100 else 100 \n",
    "      new_sol = {'TSP':route, 'KP':knapsack}\n",
    "\n",
    "      while count < max_iteration:\n",
    "            #print(\"Iteration num for bitflip_exploit: %s\" % count)\n",
    "            count += 1\n",
    "            # randomly select an idx to flip\n",
    "            idx = np.random.choice(idx_kp, 1)[0]\n",
    "            # delete select idx from selection pool\n",
    "            idx_kp = idx_kp[~(idx_kp==idx)]\n",
    "\n",
    "            # create copy of knapsack\n",
    "            kp_temp = knapsack.copy()\n",
    "            # run bit flip at idx\n",
    "            if kp_temp[idx]: # true if value at idx is 1\n",
    "                  kp_temp[idx] = 0\n",
    "            else:\n",
    "                  kp_temp[idx] = 1\n",
    "\n",
    "            # check if weight constraints is broken\n",
    "            kp_temp_weights = kp_temp * W # only gets the weights of the items in the knapsack. Those that aren't are given the value of 0\n",
    "            if np.sum(kp_temp_weights) > Q:\n",
    "                  continue\n",
    "            else:\n",
    "                  # assign new kp order to knapsack\n",
    "                  knapsack = kp_temp\n",
    "\n",
    "            # get cost of new solution\n",
    "            new_sol = {'TSP':route, 'KP':knapsack}\n",
    "            new_sol = get_fitness(new_sol, df, distances, Q) # Function from Xiao and it return solution + cost\n",
    "\n",
    "            # check if solution is not dominated by any member of Pe\n",
    "            dom = False # flag for if new solution is dominated\n",
    "            for elite_sol in P_e:\n",
    "                  if dominates(elite_sol, new_sol):\n",
    "                        dom = True\n",
    "                        continue\n",
    "\n",
    "            if dom == False:\n",
    "                  # add solution to elites if it is non-dom\n",
    "                  P_e = np.concatenate([P_e, [new_sol]])\n",
    "\n",
    "      return new_sol, P_e\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def two_opt(soln, df, distances, Q):\n",
    "      '''\n",
    "      Performs 2-opt Local Search given a solution for about min(100,n^3) moves\n",
    "      where n is the number of cities.\n",
    "      Solution format: soln = {'TSP':[], 'KP':[], 'cost': [dist,prof]}\n",
    "\n",
    "      df and distance need to be confirmed from Xiao\n",
    "      '''\n",
    "      n = len(soln) # number of cities\n",
    "      knapsack = soln['KP']\n",
    "      sol_to_beat = soln  # Store initial solution\n",
    "      route_to_beat = sol_to_beat['TSP'].copy()  # copy the initial route `tour based on what I'm getting from other functions``\n",
    "\n",
    "      #start_time = time.time()\n",
    "      improved = True # flag to indicate if solution has improved\n",
    "      # max_iterations = min(2, n ** 3) if n < 2 else 2\n",
    "      max_iterations = min(100, n ** 3) if n < 100 else 100 \n",
    "      count = 0\n",
    "      # iter = 0\n",
    "        \n",
    "      while improved and (count < max_iterations):\n",
    "            #print(\"Iteration num for two_opt: %s\" % count)\n",
    "            #count += 1\n",
    "            improved = False # Reset improved flag for each iteration\n",
    "\n",
    "            # iterate through each pair of edges in the route\n",
    "            # it automatically skips the first one since that city needs to stay the same\n",
    "            for i in range(1, len(route_to_beat) - 2):  # Time Complexity: O(n)\n",
    "                  for j in range(i + 1, len(route_to_beat)):\n",
    "                        if j - i == 1:\n",
    "                              # skip neighbouring cities e.g., (1,2) or (4,5)\n",
    "                              continue\n",
    "                        count += 1\n",
    "                        # apply 2-opt swap to generate a new route\n",
    "                        new_route = route_to_beat.copy()  # Space Complexity: O(n)\n",
    "                        new_route[i:j] = route_to_beat[j - 1:i - 1:-1]\n",
    "\n",
    "                        # calculates fitness of new solution\n",
    "                        new_sol = {'TSP':new_route, 'KP':knapsack}\n",
    "                        new_sol = get_fitness(new_sol, df, distances, Q) # this gets the cost for this solution\n",
    "                        \n",
    "                        # update old solution if it is not better than the new\n",
    "                        if ~dominates(sol_to_beat, new_sol):\n",
    "                              sol_to_beat = new_sol\n",
    "                              route_to_beat = new_route\n",
    "                              improved = True # set flag to indicate improvement\n",
    "                        \n",
    "                        if ~(count < max_iterations):\n",
    "                              break\n",
    "                        \n",
    "\n",
    "      #end_time = time.time()\n",
    "\n",
    "      #results = {'cost': sol_to_beat.cost, 'time': end_time - start_time, 'count': count, 'soln': sol_to_beat,\n",
    "      #            'max': None, 'total': None, 'pruned': None}\n",
    "\n",
    "      # return the best solution seen\n",
    "      return sol_to_beat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e82adc46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Biased Crossover\n",
    "def biased_crossover(solution1, solution2, prob=0.5):\n",
    "    KP1 = solution1['KP']\n",
    "    KP2 = solution2['KP']\n",
    "    TSP1 = solution1['TSP']\n",
    "    TSP2 = solution2['TSP']\n",
    "#   KP_likelihood = np.random.rand(len(KP_solution1))\n",
    "#   TSP_likelihood = np.random.rand(len(TSP_solution1))\n",
    "    KP_likelihood = np.random.rand(len(KP1))\n",
    "    TSP_likelihood = np.random.rand(len(TSP1))\n",
    "    cross_KP = np.array([KP1[i] if KP_likelihood[i]>prob else KP2[i] for i in range(len(KP1))])\n",
    "    cross_TSP = np.array([TSP1[i] if TSP_likelihood[i]>prob else TSP2[i] for i in range(len(TSP2))])\n",
    "    crossed_solution = {'TSP':cross_TSP, 'KP':cross_KP}\n",
    "    return crossed_solution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504c9614",
   "metadata": {},
   "source": [
    "## Initialise Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8281067a",
   "metadata": {},
   "outputs": [],
   "source": [
    "population  = initial_population(TSP_solution,KP_solution,N, weights,profit, distance_matrix, Q, alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33aad774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "999aab70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LEGION\\AppData\\Local\\Temp\\ipykernel_18848\\770429394.py:158: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  d[idx[i], j] /= costs[idx[-1], j] - costs[idx[0], j]\n"
     ]
    },
    {
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    }
   ],
   "source": [
    "# lists to contain encoded solutions for elites and non-elites\n",
    "elites_enco = []\n",
    "nonelites_enco = []\n",
    "fitness_per_iteration = []\n",
    "\n",
    "ti = 0 # Iteration counter\n",
    "\n",
    "while(ti<epoch):\n",
    "    #when ti=0, only calculate the fitness, perform non-dominated sorting to get elites and non-elites \n",
    "    if(ti==0):\n",
    "        # calculate the fitness of each solution on both objectives and add new key-value pair called 'cost'\n",
    "        for solution in population:\n",
    "              solution = get_fitness(solution, df, distance_matrix, Q)\n",
    "       \n",
    "        ### survival ###\n",
    "        \n",
    "        #converting to dataframe\n",
    "        dec_pop_DF = list_to_DF(population)\n",
    "        \n",
    "        # normalize objectives\n",
    "        # it's now a min-min problem\n",
    "        dec_pop_DF = norm_objective(dec_pop_DF)  \n",
    "        \n",
    "        # non-dominated sorting\n",
    "        dec_pop_DF, pareto_fronts = non_dominated_sorting(dec_pop_DF)\n",
    "        dec_pop_DF = calc_crowding_distance(dec_pop_DF, pareto_fronts)\n",
    "        \n",
    "        # sort population from best to worst\n",
    "        dec_pop_DF = sort_population(dec_pop_DF)\n",
    "        \n",
    "        # add fitness to list\n",
    "        fitness_per_iteration.append(dec_pop_DF['fitness'][0])\n",
    "        \n",
    "        # divide into elites and non-elits\n",
    "        elites, nonelites = elite_nonelite_sepr(dec_pop_DF, Ne)\n",
    "        print(ti)\n",
    "        \n",
    "    \n",
    "    else:\n",
    "        # lists to contain encoded solutions for elites and non-elites\n",
    "        elites_enco = []\n",
    "        nonelites_enco = []\n",
    "        \n",
    "        #Encode Elites and Non-Elites\n",
    "        for solution in elites:\n",
    "            elites_enco.append(encode_phenotype_to_genotype(solution))\n",
    "        \n",
    "        for solution in nonelites:\n",
    "            nonelites_enco.append(encode_phenotype_to_genotype(solution))\n",
    "        \n",
    "        ## Genetic Operators: Biased CRSV and Mutant Generator ##\n",
    "        \n",
    "        # populate empty list for crossover offspring\n",
    "        crsv_offspring = np.array([None]*(N-Ne-Nm))\n",
    "        \n",
    "        \n",
    "        #Perform crossover over N-Ne-Nm elements\n",
    "        for i in range(N-Ne-Nm):\n",
    "            solution1_elite = np.random.choice(elites_enco, 1)[0]\n",
    "            solution2_any = np.random.choice(np.append(elites_enco, nonelites_enco), 1)[0]\n",
    "            \n",
    "            \n",
    "            crsv_result = biased_crossover(solution1_elite, solution2_any, prob)\n",
    "            crsv_offspring[i] = crsv_result\n",
    "        \n",
    "        \n",
    "        #Mutant Generator\n",
    "        n_cities = len(nonelites_enco[0]['TSP'])+1\n",
    "        n_knapsack = len(nonelites_enco[0]['KP'])\n",
    "        mutant_offspring = mutant_generator(Nm, n_cities, n_knapsack, True)\n",
    "        \n",
    "        #### constructing the next population ####\n",
    "        \n",
    "        # select as subset of Ne from the elites\n",
    "        elites_enco = random.sample(elites_enco, Ne)\n",
    "\n",
    "        # let the non_elites be our genetic offsprings\n",
    "        nonelites_enco = np.append(crsv_offspring, mutant_offspring)\n",
    "        \n",
    "        #### Decode & Repair ####\n",
    "        \n",
    "        # decode encoded elites\n",
    "        elites_dec = np.array([None] * len(elites_enco))\n",
    "        \n",
    "\n",
    "        for i in range(len(elites_enco)):\n",
    "            temp = decode_genotype_to_phenotype(elites_enco[i])\n",
    "            elites_dec[i] = temp\n",
    "            \n",
    "        # decode encoded non-elites\n",
    "        nonelites_dec = np.array([None] * len(nonelites_enco))\n",
    "\n",
    "        for i in range(len(nonelites_enco)):\n",
    "            temp = decode_genotype_to_phenotype(nonelites_enco[i])\n",
    "            nonelites_dec[i] = temp\n",
    "        \n",
    "        # gets the solutions that violate max weight constraint\n",
    "        bad_list = []\n",
    "        for idx in range(len(nonelites_dec)):\n",
    "            if np.sum(nonelites_dec[idx]['KP']*weights) > Q:\n",
    "                bad_list.append(idx)\n",
    "        \n",
    "        # separate the good from the bad\n",
    "        bad_solutions = nonelites_dec[bad_list]\n",
    "        nonelites_dec = np.delete(nonelites_dec, bad_list)\n",
    "        \n",
    "        # repair the bad lists\n",
    "        repaired_solutions = []\n",
    "        for solution in bad_solutions:\n",
    "            repaired_sol = repair_operator(solution, weights, Q)\n",
    "            repaired_solutions.append(repaired_sol)\n",
    "\n",
    "        repaired_solutions = np.array(repaired_solutions)\n",
    "        \n",
    "        # delete bad solutions and append repaired to the nonelites_dec\n",
    "        del bad_solutions\n",
    "        nonelites_dec = np.append(nonelites_dec, repaired_solutions)\n",
    "        \n",
    "        #### Fitness Evaluation ####\n",
    "\n",
    "        # calculate the fitness of each solution on both objectives and add new key-value pair\n",
    "        for solution in nonelites_dec:\n",
    "              solution = get_fitness(solution, df, distance_matrix, Q)\n",
    "        for solution in elites_dec:\n",
    "              solution = get_fitness(solution, df, distance_matrix, Q)\n",
    "        \n",
    "        #after every freq performs 2-opt local search on the TSP and bit-flip on the KP and checks \n",
    "        #for overall improvement in fitness\n",
    "        \n",
    "        if(ti%freq == 0):\n",
    "            elites_dec = local_Search(elites_dec, weights, df, Q, distance_matrix)\n",
    "        \n",
    "        #### Survival ####\n",
    "        # we can combine our 2 lists together for it to be resorted\n",
    "        dec_pop = np.append(elites_dec, nonelites_dec)\n",
    "        \n",
    "        # convert to DF for easier fronts calculation \n",
    "        dec_pop_DF = list_to_DF(dec_pop)\n",
    "        \n",
    "        # normalize objectives\n",
    "        # it's now a min-min problem\n",
    "        dec_pop_DF = norm_objective(dec_pop_DF)\n",
    "        \n",
    "        # non-dominated sorting\n",
    "        dec_pop_DF, pareto_fronts = non_dominated_sorting(dec_pop_DF)\n",
    "        dec_pop_DF = calc_crowding_distance(dec_pop_DF, pareto_fronts)\n",
    "        \n",
    "        # sort population from best to worst\n",
    "        dec_pop_DF = sort_population(dec_pop_DF)\n",
    "        \n",
    "        # add fitness to list\n",
    "        fitness_per_iteration.append(dec_pop_DF['fitness'][0])\n",
    "        \n",
    "        # divide into elites and non-elits\n",
    "        elites, nonelites = elite_nonelite_sepr(dec_pop_DF, Ne)\n",
    "        print(ti)\n",
    "         \n",
    "\n",
    "    ti+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc0ea8bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSP</th>\n",
       "      <th>KP</th>\n",
       "      <th>cost</th>\n",
       "      <th>rank</th>\n",
       "      <th>crowding_distance</th>\n",
       "      <th>fitness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>[0, 20, 253, 123, 45, 221, 135, 228, 12, 232, ...</td>\n",
       "      <td>[0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0.9200241031709698, 0.0]</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>[34140.71937876104, -206444.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>[0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0.0, 0.9411934431941291]</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>[10601.841380878195, -33559.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>[0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0.0, 0.9411934431941291]</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>[10601.841380878195, -33559.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>[0, 3, 8, 6, 7, 9, 202, 160, 189, 203, 211, 21...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.012787469195454934, 0.4456766129339583]</td>\n",
       "      <td>0</td>\n",
       "      <td>1.255839</td>\n",
       "      <td>[10929.009632509928, -124579.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0, 54, 29, 148, 40, 252, 183, 162, 249, 138, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, ...</td>\n",
       "      <td>[0.8127141194022285, 0.1281201173735757]</td>\n",
       "      <td>0</td>\n",
       "      <td>1.227157</td>\n",
       "      <td>[31395.18629198517, -182910.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>[0, 82, 62, 262, 103, 214, 114, 193, 171, 246,...</td>\n",
       "      <td>[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.9743883218733084, 0.9645048370325608]</td>\n",
       "      <td>103</td>\n",
       "      <td>inf</td>\n",
       "      <td>[35531.6315375556, -29277.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>[0, 202, 115, 33, 6, 138, 243, 263, 69, 70, 27...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.9504597845809474, 0.9715820934524491]</td>\n",
       "      <td>103</td>\n",
       "      <td>1.372502</td>\n",
       "      <td>[34919.41831612872, -27977.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>[0, 12, 202, 65, 236, 160, 164, 150, 211, 91, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0.9660826774669946, 0.9714187721504516]</td>\n",
       "      <td>103</td>\n",
       "      <td>1.133281</td>\n",
       "      <td>[35319.13107186108, -28007.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>[0, 131, 212, 213, 109, 169, 206, 38, 162, 234...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.9765961853545476, 0.9745001007148029]</td>\n",
       "      <td>104</td>\n",
       "      <td>inf</td>\n",
       "      <td>[35588.11987187396, -27441.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>[0, 213, 119, 90, 181, 209, 160, 241, 139, 191...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1.0, 0.9794051838181254]</td>\n",
       "      <td>105</td>\n",
       "      <td>inf</td>\n",
       "      <td>[36186.90802940769, -26540.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   TSP  \\\n",
       "32   [0, 20, 253, 123, 45, 221, 135, 228, 12, 232, ...   \n",
       "44   [0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...   \n",
       "73   [0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...   \n",
       "91   [0, 3, 8, 6, 7, 9, 202, 160, 189, 203, 211, 21...   \n",
       "3    [0, 54, 29, 148, 40, 252, 183, 162, 249, 138, ...   \n",
       "..                                                 ...   \n",
       "469  [0, 82, 62, 262, 103, 214, 114, 193, 171, 246,...   \n",
       "354  [0, 202, 115, 33, 6, 138, 243, 263, 69, 70, 27...   \n",
       "188  [0, 12, 202, 65, 236, 160, 164, 150, 211, 91, ...   \n",
       "452  [0, 131, 212, 213, 109, 169, 206, 38, 162, 234...   \n",
       "193  [0, 213, 119, 90, 181, 209, 160, 241, 139, 191...   \n",
       "\n",
       "                                                    KP  \\\n",
       "32   [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, ...   \n",
       "44   [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...   \n",
       "73   [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...   \n",
       "91   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "3    [0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, ...   \n",
       "..                                                 ...   \n",
       "469  [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "354  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "188  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...   \n",
       "452  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...   \n",
       "193  [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, ...   \n",
       "\n",
       "                                           cost rank crowding_distance  \\\n",
       "32                    [0.9200241031709698, 0.0]    0               inf   \n",
       "44                    [0.0, 0.9411934431941291]    0               inf   \n",
       "73                    [0.0, 0.9411934431941291]    0               inf   \n",
       "91   [0.012787469195454934, 0.4456766129339583]    0          1.255839   \n",
       "3      [0.8127141194022285, 0.1281201173735757]    0          1.227157   \n",
       "..                                          ...  ...               ...   \n",
       "469    [0.9743883218733084, 0.9645048370325608]  103               inf   \n",
       "354    [0.9504597845809474, 0.9715820934524491]  103          1.372502   \n",
       "188    [0.9660826774669946, 0.9714187721504516]  103          1.133281   \n",
       "452    [0.9765961853545476, 0.9745001007148029]  104               inf   \n",
       "193                   [1.0, 0.9794051838181254]  105               inf   \n",
       "\n",
       "                             fitness  \n",
       "32    [34140.71937876104, -206444.0]  \n",
       "44    [10601.841380878195, -33559.0]  \n",
       "73    [10601.841380878195, -33559.0]  \n",
       "91   [10929.009632509928, -124579.0]  \n",
       "3     [31395.18629198517, -182910.0]  \n",
       "..                               ...  \n",
       "469     [35531.6315375556, -29277.0]  \n",
       "354    [34919.41831612872, -27977.0]  \n",
       "188    [35319.13107186108, -28007.0]  \n",
       "452    [35588.11987187396, -27441.0]  \n",
       "193    [36186.90802940769, -26540.0]  \n",
       "\n",
       "[500 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_pop_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ed0ec8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dec_pop_DF.to_csv('ResultDF.csv', sep=';', index=False,header=True)\n",
    "np.save('fitness_per_iteration.npy', fitness_per_iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ed96490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('[  0  20 253 123  45 221 135 228  12 232  87  14  78  40 244 252 101 158\\n  10  97  44  54 136 275  25 200  48  59 116  24  57  17 210 132 205  28\\n 100  53 250 246 189  19 108  67  64  55 257 117  92 148 240  58 263 106\\n   1  72  68 143  46 149 208 184 122  26  16 114 170 260  82 234 168 163\\n 227 181 102 238 214 174 201 172 128 180   8  56 194 231 125 237  27   9\\n 176  65  13 134   4 277 171   3 154  85 243 190 103  60 278  37 112 127\\n 235 219  41  88 207 236 209  34  76  75  52 109  62 258 113 160 186  29\\n 242  86  39  50 199 273 204 251 120 175 173 198 202 197 217 152  36  18\\n 220 153 177 187 225 147 178 146 271  30  84 247 162 130  21 188 264 107\\n 141 110 192 121 185  74 259  61 156  33  22 196 142   6  15 105 144  63\\n  90  42  73 161  83 262 245 104  99  94 211  69 230 138  23 118 226 279\\n  95 223 145 179 224 222 203 111 183 124  49 191 274 266 254 270 248 167\\n  32 150 157   5  93 164 267 133 115 193  31  81  35 268 241  38  71 119\\n  96  43 212 272 195  89 276 182  47 131 216  11  51 218 256 166 239   2\\n  77 169 137 213 249 265 155 165  79 126  80 151   7  70  98 229  66  91\\n 261 215 233 129 269 139 206 140 159 255]',\n",
       "      dtype='<U1136')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(pd.read_csv('ResultDF.csv', sep=';').iloc[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "227e554a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given string representing an array\n",
    "string_array = pd.read_csv('ResultDF.csv', delimiter=';').iloc[0,0]\n",
    "\n",
    "# Removing unnecessary characters\n",
    "string_array_cleaned = string_array.replace('[', '').replace(']', '').replace('\\n', '')\n",
    "\n",
    "# Convert the string to a NumPy array\n",
    "np_array = np.fromstring(string_array_cleaned, sep=' ', dtype=np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7153005a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the CSV file with ';' as the delimiter and custom converters\n",
    "def convert_to_array_int(x):\n",
    "      try:\n",
    "            string_arr_cleaned = x.replace('[', '').replace(']', '').replace('\\n', '')\n",
    "            np_arr = np.fromstring(string_arr_cleaned, sep=' ', dtype=np.int64)\n",
    "            \n",
    "            return np_arr\n",
    "      except ValueError or SyntaxError:\n",
    "            return x\n",
    "\n",
    "def convert_to_array_float(x):\n",
    "      try:\n",
    "            string_arr_cleaned = x.replace('[', '').replace(']', '').replace('\\n', '')\n",
    "            np_arr = np.fromstring(string_arr_cleaned, sep=' ')\n",
    "\n",
    "            return np_arr\n",
    "      except ValueError or SyntaxError:\n",
    "            return x\n",
    "\n",
    "converters = {\n",
    "    'TSP': convert_to_array_int,\n",
    "    'KP': convert_to_array_int,\n",
    "    'cost': convert_to_array_float,\n",
    "    'fitness': convert_to_array_float\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c6a37dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the CSV file using the custom converters\n",
    "dec_pop_DF = pd.read_csv('ResultDF.csv', delimiter=';', converters=converters)\n",
    "\n",
    "fitness_per_iteration = np.load('fitness_per_iteration.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a3eacef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, ...\n",
       "1      [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...\n",
       "2      [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...\n",
       "3      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
       "4      [0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, ...\n",
       "                             ...                        \n",
       "495    [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...\n",
       "496    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
       "497    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...\n",
       "498    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
       "499    [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, ...\n",
       "Name: KP, Length: 500, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_pop_DF['KP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52041052",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=[]\n",
    "y1=[]\n",
    "\n",
    "for i in dec_pop_DF['fitness']:\n",
    "    x1.append(i[0])\n",
    "    y1.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b120e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3339c7bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1fe4df9c460>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97cc89e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1fe4f1b1f90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5121b5ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Profit of Knapsack')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(-pd.DataFrame(fitness_per_iteration)[1][::100])\n",
    "plt.xlabel('No of Iterations')\n",
    "plt.ylabel('Profit of Knapsack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0cf6ac34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Time of tour')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(pd.DataFrame(fitness_per_iteration)[0][::100])\n",
    "plt.xlabel('No of Iterations')\n",
    "plt.ylabel('Time of tour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43e97d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSP</th>\n",
       "      <th>KP</th>\n",
       "      <th>cost</th>\n",
       "      <th>rank</th>\n",
       "      <th>crowding_distance</th>\n",
       "      <th>fitness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0, 20, 253, 123, 45, 221, 135, 228, 12, 232, ...</td>\n",
       "      <td>[0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, ...</td>\n",
       "      <td>[0.9200241, 0.0]</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>[34140.71937876, -206444.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0.0, 0.94119344]</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>[10601.84138088, -33559.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...</td>\n",
       "      <td>[0.0, 0.94119344]</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>[10601.84138088, -33559.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0, 3, 8, 6, 7, 9, 202, 160, 189, 203, 211, 21...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "      <td>[0.01278747, 0.44567661]</td>\n",
       "      <td>0</td>\n",
       "      <td>1.255839</td>\n",
       "      <td>[10929.00963251, -124579.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0, 54, 29, 148, 40, 252, 183, 162, 249, 138, ...</td>\n",
       "      <td>[0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, ...</td>\n",
       "      <td>[0.81271412, 0.12812012]</td>\n",
       "      <td>0</td>\n",
       "      <td>1.227157</td>\n",
       "      <td>[31395.18629199, -182910.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>[0, 82, 62, 262, 103, 214, 114, 193, 171, 246,...</td>\n",
       "      <td>[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.97438832, 0.96450484]</td>\n",
       "      <td>103</td>\n",
       "      <td>inf</td>\n",
       "      <td>[35531.63153756, -29277.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>[0, 202, 115, 33, 6, 138, 243, 263, 69, 70, 27...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
       "      <td>[0.95045978, 0.97158209]</td>\n",
       "      <td>103</td>\n",
       "      <td>1.372502</td>\n",
       "      <td>[34919.41831613, -27977.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>[0, 12, 202, 65, 236, 160, 164, 150, 211, 91, ...</td>\n",
       "      <td>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...</td>\n",
       "      <td>[0.96608268, 0.97141877]</td>\n",
       "      <td>103</td>\n",
       "      <td>1.133281</td>\n",
       "      <td>[35319.13107186, -28007.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>[0, 131, 212, 213, 109, 169, 206, 38, 162, 234...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...</td>\n",
       "      <td>[0.97659619, 0.9745001]</td>\n",
       "      <td>104</td>\n",
       "      <td>inf</td>\n",
       "      <td>[35588.11987187, -27441.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>[0, 213, 119, 90, 181, 209, 160, 241, 139, 191...</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, ...</td>\n",
       "      <td>[1.0, 0.97940518]</td>\n",
       "      <td>105</td>\n",
       "      <td>inf</td>\n",
       "      <td>[36186.90802941, -26540.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   TSP  \\\n",
       "0    [0, 20, 253, 123, 45, 221, 135, 228, 12, 232, ...   \n",
       "1    [0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...   \n",
       "2    [0, 8, 6, 7, 9, 202, 160, 189, 203, 211, 214, ...   \n",
       "3    [0, 3, 8, 6, 7, 9, 202, 160, 189, 203, 211, 21...   \n",
       "4    [0, 54, 29, 148, 40, 252, 183, 162, 249, 138, ...   \n",
       "..                                                 ...   \n",
       "495  [0, 82, 62, 262, 103, 214, 114, 193, 171, 246,...   \n",
       "496  [0, 202, 115, 33, 6, 138, 243, 263, 69, 70, 27...   \n",
       "497  [0, 12, 202, 65, 236, 160, 164, 150, 211, 91, ...   \n",
       "498  [0, 131, 212, 213, 109, 169, 206, 38, 162, 234...   \n",
       "499  [0, 213, 119, 90, 181, 209, 160, 241, 139, 191...   \n",
       "\n",
       "                                                    KP  \\\n",
       "0    [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, ...   \n",
       "1    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...   \n",
       "2    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, ...   \n",
       "3    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
       "4    [0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, ...   \n",
       "..                                                 ...   \n",
       "495  [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, ...   \n",
       "496  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
       "497  [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ...   \n",
       "498  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...   \n",
       "499  [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, ...   \n",
       "\n",
       "                         cost  rank  crowding_distance  \\\n",
       "0            [0.9200241, 0.0]     0                inf   \n",
       "1           [0.0, 0.94119344]     0                inf   \n",
       "2           [0.0, 0.94119344]     0                inf   \n",
       "3    [0.01278747, 0.44567661]     0           1.255839   \n",
       "4    [0.81271412, 0.12812012]     0           1.227157   \n",
       "..                        ...   ...                ...   \n",
       "495  [0.97438832, 0.96450484]   103                inf   \n",
       "496  [0.95045978, 0.97158209]   103           1.372502   \n",
       "497  [0.96608268, 0.97141877]   103           1.133281   \n",
       "498   [0.97659619, 0.9745001]   104                inf   \n",
       "499         [1.0, 0.97940518]   105                inf   \n",
       "\n",
       "                         fitness  \n",
       "0    [34140.71937876, -206444.0]  \n",
       "1     [10601.84138088, -33559.0]  \n",
       "2     [10601.84138088, -33559.0]  \n",
       "3    [10929.00963251, -124579.0]  \n",
       "4    [31395.18629199, -182910.0]  \n",
       "..                           ...  \n",
       "495   [35531.63153756, -29277.0]  \n",
       "496   [34919.41831613, -27977.0]  \n",
       "497   [35319.13107186, -28007.0]  \n",
       "498   [35588.11987187, -27441.0]  \n",
       "499   [36186.90802941, -26540.0]  \n",
       "\n",
       "[500 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_pop_DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f8f298dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSP</th>\n",
       "      <th>KP</th>\n",
       "      <th>cost</th>\n",
       "      <th>crowding_distance</th>\n",
       "      <th>fitness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TSP   KP  cost  crowding_distance  fitness\n",
       "rank                                            \n",
       "0     151  151   151                151      151\n",
       "1       3    3     3                  3        3\n",
       "2       1    1     1                  1        1\n",
       "3       3    3     3                  3        3\n",
       "4       2    2     2                  2        2\n",
       "...   ...  ...   ...                ...      ...\n",
       "101     4    4     4                  4        4\n",
       "102     4    4     4                  4        4\n",
       "103     4    4     4                  4        4\n",
       "104     1    1     1                  1        1\n",
       "105     1    1     1                  1        1\n",
       "\n",
       "[106 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_pop_DF.groupby('rank').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f2d6bf8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=[]\n",
    "y1=[]\n",
    "\n",
    "for i in dec_pop_DF.query('rank == 0')['fitness']:\n",
    "      x1.append(i[0])\n",
    "      y1.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0d2e377",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.array(x1)\n",
    "y1 = np.array(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bd3d312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rank 0 Solutions on Objective Space')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, -y1)\n",
    "plt.xlabel(\"Time of tour\")\n",
    "plt.ylabel(\"Profit of KP\")\n",
    "plt.title('Rank 0 Solutions on Objective Space')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "34bd45fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rank 0 Solutions on Objective Space')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, -y1)\n",
    "plt.xlim(0,12500)\n",
    "plt.ylim(0,50000)\n",
    "plt.xlabel(\"Time of tour\")\n",
    "plt.ylabel(\"Profit of KP\")\n",
    "plt.title('Rank 0 Solutions on Objective Space')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6469b7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, -y1)\n",
    "plt.xlim(10000,12500)\n",
    "plt.ylim(30000,40500)\n",
    "plt.xlabel(\"Time of tour\")\n",
    "plt.ylabel(\"Profit of KP\")\n",
    "plt.title('Rank 0 Solutions on Objective Space');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3ca8d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x1, -y1)\n",
    "plt.xlim(10500,11000)\n",
    "plt.ylim(33000, 38000)\n",
    "plt.xlabel(\"Time of tour\")\n",
    "plt.ylabel(\"Profit of KP\")\n",
    "plt.title('Rank 0 Solutions on Objective Space');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87266104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x1)\n",
    "plt.xlabel(\"Time of tour\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title('Distribution of Solutions based on Time of Tour');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "846bb1fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(-y1)\n",
    "plt.xlabel(\"\\nProfit of Knapsack\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title('Distribution of Solutions based on Profit of Knapsack');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame({'time':x1, 'profit':-y1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9e492ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results.sort_values(by=['time','profit'])[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bad007f",
   "metadata": {},
   "source": [
    "#### Results of JoMar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "813538ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data from the text file\n",
    "jomar_answer = np.loadtxt('./jomar_a280_n279.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "72fa9c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(jomar_answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eecba76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "df474e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1fe501a0d60>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(results['time'], results['profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7b5d2b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "jomar_df = pd.DataFrame(jomar_answer, columns=['time','profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "675dfd91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1fe511bf7c0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(jomar_df['time'], jomar_df['profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3e5e6080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1fe4fc3e320>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(jomar_df['time'], jomar_df['profit'])\n",
    "plt.scatter(results['time'], results['profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e59620",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}