tsp_GA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, random, operator, pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_starting_population(size,Number_of_city):\n",
    "    '''Method create starting population \n",
    "    size= No. of the city\n",
    "    Number_of_city= Total No. of the city\n",
    "    '''\n",
    "    population = []\n",
    "    \n",
    "    for i in range(0,size):\n",
    "        population.append(create_new_member(Number_of_city))\n",
    "        \n",
    "    return population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pick_mate(N):\n",
    "    '''mates are randomaly picked \n",
    "    N= no. of city '''\n",
    "    i=random.randint(0,N)    \n",
    "    return i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(i,j):\n",
    "    '''\n",
    "    Method calculate distance between two cities if coordinates are passed\n",
    "    i=(x,y) coordinates of first city\n",
    "    j=(x,y) coordinates of second city\n",
    "    '''\n",
    "    #returning distance of city i and j \n",
    "    return np.sqrt((i[0]-j[0])**2 + (i[1]-j[1])**2)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_population(population, CityList):  \n",
    "    '''\n",
    "    Score of the whole population is calculated here\n",
    "    population= 2 dimensional array conating all the routes\n",
    "    Citylist= List of the city \n",
    "    '''\n",
    "    scores = []\n",
    "  \n",
    "    for i in population:\n",
    "        #print(i)\n",
    "        scores.append(fitness(i, CityList))\n",
    "        #print([fitness(i, the_map)])\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitness(route,CityList):\n",
    "    '''Individual fitness of the routes is calculated here\n",
    "    route= 1d array\n",
    "    CityList = List of the cities\n",
    "    '''\n",
    "    #Calculate the fitness and return it.\n",
    "    score=0\n",
    "    #N_=len(route)\n",
    "    for i in range(1,len(route)):\n",
    "        k=int(route[i-1])\n",
    "        l=int(route[i])\n",
    "\n",
    "        score = score + distance(CityList[k],CityList[l])\n",
    "        \n",
    "        \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_new_member(Number_of_city):\n",
    "    '''\n",
    "    creating new member of the population\n",
    "    '''\n",
    "    pop=set(np.arange(Number_of_city,dtype=int))\n",
    "    route=list(random.sample(pop,Number_of_city))\n",
    "            \n",
    "    return route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crossover(a,b):\n",
    "    '''\n",
    "    cross over \n",
    "    a=route1\n",
    "    b=route2\n",
    "    return child\n",
    "    '''\n",
    "    child=[]\n",
    "    childA=[]\n",
    "    childB=[]\n",
    "    \n",
    "    \n",
    "    geneA=int(random.random()* len(a))\n",
    "    geneB=int(random.random()* len(a))\n",
    "    \n",
    "    start_gene=min(geneA,geneB)\n",
    "    end_gene=max(geneA,geneB)\n",
    "    \n",
    "    for i in range(start_gene,end_gene):\n",
    "        childA.append(a[i])\n",
    "        \n",
    "    childB=[item for item in a if item not in childA]\n",
    "    child=childA+childB\n",
    "    \n",
    "    \n",
    "    \n",
    "       \n",
    "    return child\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mutate(route,probablity):\n",
    "    '''\n",
    "    mutation \n",
    "    route= 1d array\n",
    "    probablity= mutation probablity\n",
    "    '''\n",
    "    #for mutating shuffling of the nodes is used\n",
    "    route=np.array(route)\n",
    "    for swaping_p in range(len(route)):\n",
    "        if(random.random() < probablity):\n",
    "            swapedWith = np.random.randint(0,len(route))\n",
    "            \n",
    "            temp1=route[swaping_p]\n",
    "            \n",
    "            temp2=route[swapedWith]\n",
    "            route[swapedWith]=temp1\n",
    "            route[swaping_p]=temp2\n",
    "    \n",
    "    return route\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def selection(popRanked, eliteSize):\n",
    "    selectionResults=[]\n",
    "    result=[]\n",
    "    for i in popRanked:\n",
    "        result.append(i[0])\n",
    "    for i in range(0,eliteSize):\n",
    "        selectionResults.append(result[i])\n",
    "    \n",
    "    return selectionResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rankRoutes(population,City_List):\n",
    "    fitnessResults = {}\n",
    "    for i in range(0,len(population)):\n",
    "        fitnessResults[i] = fitness(population[i],City_List)\n",
    "    return sorted(fitnessResults.items(), key = operator.itemgetter(1), reverse = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def breedPopulation(mating_pool):\n",
    "    children=[]\n",
    "    for i in range(len(mating_pool)-1):\n",
    "            children.append(crossover(mating_pool[i],mating_pool[i+1]))\n",
    "    return children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mutatePopulation(children,mutation_rate):\n",
    "    new_generation=[]\n",
    "    for i in children:\n",
    "        muated_child=mutate(i,mutation_rate)\n",
    "        new_generation.append(muated_child)\n",
    "    return new_generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matingPool(population, selectionResults):\n",
    "    matingpool = []\n",
    "    for i in range(0, len(selectionResults)):\n",
    "        index = selectionResults[i]\n",
    "        matingpool.append(population[index])\n",
    "    return matingpool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def next_generation(City_List,current_population,mutation_rate,elite_size):\n",
    "    population_rank=rankRoutes(current_population,City_List)\n",
    "    \n",
    "    #print(f\"population rank : {population_rank}\")\n",
    "    \n",
    "    selection_result=selection(population_rank,elite_size)\n",
    "    #print(f\"selection results {selection_result}\")\n",
    "    \n",
    "    mating_pool=matingPool(current_population,selection_result)\n",
    "    #print(f\"mating pool {mating_pool}\")\n",
    "    \n",
    "    children=breedPopulation(mating_pool)\n",
    "    #print(f\"childern {children}\")\n",
    "    \n",
    "    next_generation=mutatePopulation(children,mutation_rate)\n",
    "    #print(f\"next_generation {next_generation}\")\n",
    "    return next_generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def genetic_algorithm(City_List,size_population=1000,elite_size=75,mutation_Rate=0.01,generation=2000):\n",
    "    '''size_population = 1000(default) Size of population\n",
    "        elite_size = 75 (default) No. of best route to choose\n",
    "        mutation_Rate = 0.05 (default) probablity of Mutation rate [0,1]\n",
    "        generation = 2000 (default) No. of generation  \n",
    "    '''\n",
    "    pop=[]\n",
    "    progress = []\n",
    "    \n",
    "    Number_of_cities=len(City_List)\n",
    "    \n",
    "    population=create_starting_population(size_population,Number_of_cities)\n",
    "    progress.append(rankRoutes(population,City_List)[0][1])\n",
    "    print(f\"initial route distance {progress[0]}\")\n",
    "    print(f\"initial route {population[0]}\")\n",
    "    for i in range(0,generation):\n",
    "        pop = next_generation(City_List,population,mutation_Rate,elite_size)\n",
    "        progress.append(rankRoutes(pop,City_List)[0][1])\n",
    "    \n",
    "    \n",
    "    rank_=rankRoutes(pop,City_List)[0]\n",
    "    \n",
    "    print(f\"Best Route :{pop[rank_[0]]} \")\n",
    "    print(f\"best route distance {rank_[1]}\")\n",
    "    plt.plot(progress)\n",
    "    plt.ylabel('Distance')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.show()\n",
    "    \n",
    "    return rank_, pop\n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "cityList = []\n",
    "\n",
    "\n",
    "for i in range(0,25):\n",
    "    x=int(random.random() * 200)\n",
    "    y=int(random.random() * 200)\n",
    "    cityList.append((x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial route distance 2040.384360767298\n",
      "initial route [8, 6, 11, 2, 3, 9, 7, 10, 22, 14, 13, 4, 19, 5, 17, 12, 18, 21, 23, 24, 0, 16, 20, 15, 1]\n",
      "Best Route :[ 6  1 16  7 14  9 13 21 12 15 20  0  2 22 10  3  4  8  5 17 11 18 23 19 24] \n",
      "best route distance 1981.5676911145292\n"
     ]
    },
    {
     "data": {
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AJQB+AHAlgA8A3OJ0AhG1I6IpRLSQiOYT0TV6ejMimkRES/X/TfV0IqIHiWgZ\nEX1PRIeayhqh519KRCP8/FAVxFIlCEISqV+7UDlvFIqjSDFfPQDjmPlJACCiQj1tp8M55QCuY+ZZ\nugfWTCKaBOASAJ8w8xgiGg1NKf0NwMkAOut/RwB4FMARRNQMwG0A+kAzj80kovHMvMnbT3VHVo4L\ngpBEmtSvFbcIGaiOOD6BpigM6gGY7HQCM69j5ln6520AFgJoA2AYgOf0bM8BOF3/PAzA86wxDUAT\nImoNYDCAScy8UVcWkwAMUZTbEzLiEAQhHw7dr0ko5V53UhfH46f2bJ36nKQ5jrrMvN34on+ur1oJ\nEbUH0BvAtwBaMfM6vZx1SHtntQGwynTaaj3NLl0QBCFRDD5on1DKrediqjpo38apz0ma49iRNedw\nGIBdKicSUQMAbwIYxcxbnbJapLFDenY9VxDRDCKaUVJSoiKaIAhCtcBsLUnSyvFRAF4noi+J6EsA\nrwK4yu0kIqoFTWm8yMxv6ck/6yYo6P836OmrAbQznd4WwFqH9AyY+Qlm7sPMfVq0aKH4s7Ll9XWa\nIChzfBd/z6ZQNYirDcmoNikjDmb+DkBXAH8E8CcA3Zh5ptM5pK2mexrAQmYeazo0HoDhGTUCwLum\n9It176p+ALbopqyJAAYRUVPdA2uQniYIVQ7pmwhhkDniSM4cBwD0BdAT2lzFeUR0sUv+/gAuAjCA\niObof6cAGAPgJCJaCuAk/TugufiuALAMwJPQFBSYeSOAOwB8p//9Q08LHDevqiM6NMPv+ncIo+pY\nuPzo6vNbqgoSnaB6E5ZnpttjY643Me64RPQCgAMAzAFQoSczgOftzmHmr2DfwTrRIj9DC21iVdY4\nAONUZM0Ht5tTp1YhzjysDcZ9vTJsUWzp0aYR5q1xmipSZ8RR7fHUV/H9FhU6ttgLK6rBHgkGBaI3\nhBCIeo5DdR1HHwDdOYqwiwkn7rUe3VsHpzhUOr+1iwpQVl4ZSH1+GN6rDe6btCS2+oNHNIfgHS/t\nTmKi4wKYByAcP7MEURVe6agVV92ieLdsqW6Wner2e6Lij8cfELcISsQ2OR5xxaojjuYAFhDRdACl\nRiIznxaKVDHhdvEJ8b/4Qdav8rDVrVWIrbvLg6vUI9VtTkBMVYIfrF6DfRrVxfqtu7XjpvQkmapu\nD1OIqgKjeikOFerWUo+REwYFcV/wgInb1FlVqSpXres+jXLSDmnbGHNXbwm13ow5jgS5435u9Re2\ncFGj8nDG/+IHV79KSef0beeeSVCmmunB0Bg1sHPG97Cu2yfXHRdoeUd3bp6T1nu/pnmX6/bzzcev\nybp2YaC6A2A/IvqOiLYTURmVFTBSAAAgAElEQVQRVRBRMDO0CcLd5S3+Fz+K+v91xsGpzy0a1Am/\nQgeSZtq5frBzzCA34n5+kspNp3TN+F6rMJq5tdaN64ZafpdWDXFAi71CrSObE7qEv8ee6t35L4Dz\nACyFFuDwcj2tWqFiT4/7vQ+yfrufmzHUjfkHi6mqZuBmXgnjup3TJ5rRdJ0AzL1ubVPU7q7Kap2Z\nlwEoZOYKZn4GwPGhSZVg4m7Hoq4/7mYu7uudTb6ujge2ahiQJGmOsTCPVHXCvu+92jXBv8/qGYki\nH947/JisUS+UUFUcO4moNoA5RHQ3EV0LINrxV2LI70Ebkmf0zCAfdLuyzC9tEr2aLuy3X2x1u72g\nDes4+5uc2C14M8ILlx0ReJlh09tj+PGkPIavXXmkp/xE/sxud5zeI7MczyWEi+ovukjPexWAHdCC\nDp4RllBJJt8H+OC2jXF0J/89xKhfoLjnGKwUV/u9k9tnKYj7glUR2jXN3JUhWx9HZdLz+j4VFUYj\nl9c2IqmmqtOZeTczb2XmvzPzXwCcGqZgSYQof82fpMX3di8NKeSpqSTn7lVtsp+r7Nci+3hSHsOo\n5Miux+09jLpdUVUcVvt8XxKgHFWasw9rq5y3KCJvEVWuPLaj4/G4J3OtOvB+zGdBzQO4vZ9xdQz+\n79TusdQbFjl3OCE9mKhMtzmKMyG/38CxFSOi84joPQAdiGi86e8zAL9GImHCyL6BDesWoXOrBkrn\nnnVYW1xyVPsQpPLPxS7yxP28WlXvR6Tf9e/gOv+gglvI6qE993U8Htb1vCzCSMf/ObdX3mVkX4bs\n6xrVc+e1njhfh0PaNrY9lrTJ8W8A3Adgkf7f+PsLQtr3O+lkPzh1igqVb9oNg7vEvhLbjN1LYPUy\nDejaEucdHv2kdBQ9rcb1ainndVsQecewg3LSshezJZV7zuqplG9YL3svoX31dRHjr+rvWEb2fXV3\nxw0Wu8dqUPdWmHvboIBr847VSL9N03q2+Q3FO6h7q9BkMuOoOJj5R2b+DMBAAF/qq8XXQduFL1lj\npwiwWgC4T2MPC+RiumJBjHLq1y5Ei4bhLQZ86fdH4IXLDsd3Nw/MSI+i53nv2Ye45nlnZH8UjxmK\n1o3tX17A2hTZqlF6kVncpr+oaOAyunNfCR2RSSirnmZ71XbsSETxPP7n3F4Z9bz5x6MAAP8+016x\nG4q3ffNoHEdUDe5fAKhLRG0AfALgUgDPhiVUUtE2QI/3xfczJB3Q1cYF1HZyPH3A6Bmy38oVOeqA\n5jimc4sc5WRpqvJzCxzOqaMQAbhXO2/uozUV43kpdPMuczucUP0axfvfsXmm6fuw/bWQJQ3r2is0\n482M6rKpGn2JmXcS0WUAHmLmu4lodpiCJZVcb4+EPuEm8nkJk//r8ufHjTtDLb+qeKkFYRY0nAMK\nCwgDu7VEk/q18cbM1crn2cuWt2hZ9YVTblCoynVp//Zo17Q+So09cyL6PaojDiKiIwFcAGCCnpb/\nTGMVI9/eb9BK5oFz1CYp7cJ2eArnoTDYGNZrX9ytaCdXJoo3O0Eu0nFSt5Z9c9BRId5SB5OZhIjw\n1Ii+OO7AFpZ5s9+FOEKOWOEqh08xurRqqHQNjTpUlfhtvzkIvzu6QyT7jJtRVRyjANwI4G1mnk9E\nHQFMCU+s5GJ1P+NqdlQfxHq1rSfkbRVHxspxdXmOOmBv/Dbg+D9BeVU51pHUbmfEnNKjdUbjbzDr\n/07CwW3sPXoM9mkUXsDAoG+RUV5Ud37itcfi0+uOV8prXi+m+rtTI6iIfpGXsOqnMfO/9e8rmPnP\n4YqWTOJsZF66PDO0hGpHea/a1oNDL79EpUcTxkNrdbn7+1h57yRZlLc0zMFN99a5e0F4wW7Ve7O9\navuW2+60nAWA/orPmyR2GgiU6tR5ve5R/Ry3dRwP6P/fy1rHMZ6IxkcjYrKwui9RWToO2te912dF\nSxtvKBVTlaEMmON5ua2UUeP6tXKUqGs5Dr81ynmqME0KH1xzTN5lxNWMJsVa6HZ/omiYCwoUnAuy\nqK9bFdy82YLCrZYX9P/3hi1IVcCq8fFye/N+6Hye33Sv2p7KS/pkbtANfXUZcSQB1Z/n9ZJHdYvC\nnmuZ/JdjsXrTLlzyzHeOdRR5VBwX9tsfpeWVuLR/+7zkU8VtHcdM/f/nABYAWFCddwB0g5lt5jiq\nZmtQQOohMszZzujdBs9c0jckqTLZq47NgkmP72+vtvbutGHHJTQ/M5XVWHOY3wPjJys/XwH09O8a\n3sM9U3a5ns+w5vrBXTDjloGu+Tq1bIjjXTZaKiDvwRRrFRbgD8cdgDpF0SwwdjNVERHdTkS/QFs9\nvoSISojo1kikSyA55p0Iu6tEmS9Yvk2QnfnGnG7OYawHuaR/e5xgtzYkAI7suHfqc63CAiy58+S8\nyrvs6A5oXF99dXi+XHzk/hnfjbbz0P2apD73dAgfUVUhUGrviUYuq/E9h/pQOOGCI/bH0INbeyo3\ne17H7Z2yE6NNk3poHtBumURAUYH79HOc1gA36UYB6A+gLzPvzcxNARwBoL++J0eNgogsbY+qncik\nWX289rR779cUxWOGoqdD7z0IXr6iH07ukd63pLbCAr18qIxgELDojiF47cojUyOOpD0LKfIQrLCA\n8NdBXbDgH4Mjs7Vn42f0f6jHvUGsCLIRt2tnsolzd0y3N/JiAOcx80ojgZlXALhQP1bjKLS4WSor\nj4MgcDdUhRKNn5skc5zmrhjc1bBS/E5rGrxCBNStVYiiwoL0Ct8kTh7BeXWyG0RaD76+jRdfRl6P\n6ziCZv6arb7Os7tttQOMel1A3uc4osbt19Zi5l+yE5m5BEB0Y/8EUZhleyQAFx25fyS70qk2NnuZ\n1m300cMVWJdnk27xLS7TfBD1ul01q3mH96+291Dy6tFlxrD5J1Rv4LELD7VMV7kNXnrAQbvj7m3n\nAGJDWUWlZbqfyfFRAztjkGlnz38OPxj/OuNgT/KYKSC1DcEUrFmh4VZ1mc9j1RbrEUch/nJSl8hl\nsZt47NO+Weqz3cpdwFvjpfRiBzpcz++4F6x+W6eW9qHyj+rUHI3qqptizI2NccviMjMcbno2rHAL\n4uiEpygKLnm9uKM+fP6heFePxusl0rGBF6VVy2LSetTAAzPkPf+I/XBIHuZc1dF0nOGO3BTHIUS0\n1eJvGwD/KrWKQsh9oL29LPnd6CAek7+flg77bdd4Ze45nl99Byiubo8TP5svOd3L7OLMZj5jPsXt\nsjqZP9/845F44bLD3US0JsS2xpsyzB5yZF60vWoX4e4ze+K0Q7T9TZyKHtqzNdrqW9HePLQ7hph6\n/0Fyx+k90LGF2t47+WD+rVc4bLQWpzXLzR23kJkbWfw1ZOYaZ6qqX6cox/ZIWf+D5M7Te+DRC9Km\ngyA6qUN7pr1O7IqrZWGv9Wsycnrw/cIc7PX289v83gtDSTk1snee3gNPj7B3dz5s/2Y4prP9SDIu\nrBoy1Wtrle23fduhtb6/h2rvukGdIlx2TAe1Si3lsBf4on772x4LEuPRKB4zFDed0s0hX3JHHAI0\n99C/DjoQdww7yPOKTjec7v2F/fZH84D3wDBXZ9V4DTloH5xysJVyUQk5ksmogZ1x5qHq2+rGha8R\nh9OxbI9tU+6UB5fLfe/faW/ceHLXVNoLlx2Obq0boaGLieyvgw50PO4Xu2t03Unp+rw0ZK5Z4/N6\nj5zBB2VuvqQ6ckuyO64A7QZdNaAzmtSv7bD2QaEcizQv25kSKKMHp9LcOeWxkvmqAZ0ylGM+vZpR\nAw/Me4/1ILy53H6CUcN+zep7KFPdVJVZlzHicC//yuMOSH3v2aYJPrzmGPxw+2DH864a0Nnx9wbd\n1lx9Ynp3w3z6VDnXLDlOfKHQqlG6Q/j4RX0ynFhUX7k4dakojiwePK83Jl17bEaalw6p+aar7M0c\naC9NIV/G4j4PdUftVRXlxF9q3sHLfJXPuvxGMaUq8KZarnGy0QDZOe3yRa4/Iqow22vP/Oypjzji\nUx01bk8NN4zJOFWMm2cOBmigsgjK1XvIkzTBE3f9dgT50qjMO+TW77cu7b9XV0qv086xBKQMdBWc\n49cqT52sdUKZu26qlZHYyXHBmh5tGmFYL93bw0i0uIkxRicJnKAbImMfZVW6tGoYsARp/O0G5+9m\npleOezs/KPfdMJ/BvNZx2Jiq/Mw/JYEOzfdCi4Z1cKPD5LYZXyOOBLvjCsgdRr9/9TG40MbDwuuL\nWekh3oXry6ZShtf8ZNQV7AvcrXVDnKHHNXLCqHbitceieQNtkRfDOtikX9LzDuqF+u3tpVeOezsv\nzvASqgTtVWU+P6qfH9RTXq92Ib67eaDjOiozZxyafhdUw7XI5Hg1wOomtmxovSPaqIHpCcUgtqrs\n7LBYzY3sOFDZ8rTSd3Xrvq/1JkFBBXbLIYSXom8H68VvQ3vui0Z1i3BuX233QpWX3e+SBeOjXYgK\nuxXQVUBvoOs+6htJuYYccfBMC5okDGrO6bsfVvzzFKz81ymoW0stwq2441ZB7B42863s0aYx3h3Z\nP2fth3llt7mYD685Bu9ffbRtnVYvz+S/HIs3LMw+Vw/oZF1GVhEtG9bFUxf3Qdum1iuGjd9w7cBc\nN89Przsux5FAlbCH2URIubMaL9h/z++NDy02O2rTpB6+v31waiteldGEX/n7d2qOy4/uYBuSYvJf\njrOuL8GKY+YtA/Gfc3vhSg9rdpS3RPUpU77cc1bPyOssKCBPyqBaznEQ0Tgi2kBE80xphxDRVCL6\nQd9VsJHp2I1EtIyIFhPRYFP6ED1tGRGNDkteJ/o6hGkw7rPdPTykXZOUt4lVY2OOk9StdSP0yNnb\n2XnSrFPLhjlhFtrv7W219sDurdBEDztu5d1ySLsmlm61HVs0yNgkKoweUKY03srP/iX1axehm8P2\nqpV6+CKV3+HXTbiwgHDLqd3R0mZ/brtNt4I2VXktzvi1d5yeu+fF3g3qYFivNpbxlfa3eRZVvaqM\n0C92HZugMUyyZ/dpF2o9QdzN6mqqehbAkKy0pwCMZuaDAbwN4HoAIKLuAM4FcJB+ziNEVEhEhQAe\nBnAygO4AztPzRsan1x2HURa9bTubv9fGM+iNfZ66uA9uPKWre8YE4PuXe1w5rppXYW1eDscq2rDz\nxY/iuGFI8PHTvMaCOswmyKbqe3L2YW3xzsj+GBxSGJGqTJzzXqG54zLzF0TUPiu5C4Av9M+TAEwE\n8H8AhgF4hZlLAawkomUAjGA8y/RQ7iCiV/S8C8KSOxu32DTGKMJvb9tqbvzFy49IhzKxsI87cULX\nlo6r2+P0xDAT9jNP8G67TkeuVRfuqhM64YslJd4q8oGfy2UVIj0p91+VosIC9GoX7v4vcZLPviU1\naQHgPACn6Z/PBmCMB9sAWGXKt1pPs0vPgYiuIKIZRDSjpCT8F9kXll4nua1b/07NcVSn5rmnE2X1\n0oMbrVitQwkbpwffOGY/snMvP2X+UHzDDCXuxXYcVacv6Hr8FhfGz33tyiMTs1I8IWIoUZMmx38H\nYCQRzQTQEOnQ7FZXgB3ScxOZn2DmPszcp0WL8M0HLk4g6uW4xv/3x+XHdMTpvfbFJdmb19sUGMQz\naBTx6AWH4uXf93PN7/UlzZTRw5oBxbxe9sr45/CD0aH5XoF4lTmFcDfw00j4nTwdruAmHSTd920U\naIOdz6N85bEHuOa59dTueOQC631LvOLXzf3cvu3wwDnukSnCItKV48y8CMAgACCiAwEM1Q+tRnr0\nAQBtAazVP9ulJwNjctzhaXU6VhFwF9+oqnG9Wnjg3N62+ex2GMtHGuPckz3u+xwGROTdVGWcq9D0\nnNitFU7s1gqrN+20KMdbxZP/chzaj57g6Zx8ISLbXsv95/TC27PXZCYG3BVPopfYW386ytbt3Mzv\nju4QgTTOjDkzeq8vM5GOOIiopf6/AMAtAB7TD40HcC4R1SGiDgA6A5gO4DsAnYmoAxHVhjaBPj5K\nme1QccfNp5xUeaY3LEr3uyj20XA0VQXYsii7fvoIB6IiZ9RtZHYYnMxjEQujgDYfVZWMREJoIw4i\nehnA8QCaE9FqALcBaEBEI/UsbwF4BgCYeT4RvQZt0rscwEhmrtDLuQraJHohgHHMPD8smf2QmsRW\naB7yeWl7tm2c00hZvWtB7Zz37lVHY/NOb5s8elaaHvN7Oc/Ppa70MTlulTPuCWgVk1vc+iPuPccz\n6o6v6ipLmF5V59kc+o9N/rsA3GWR/gGADwIULVS8KoeLj9wfz0/90fa44b9+eq9w7c7ZYjeoU5SX\nx4drfXm2XEqT46m4UGr4cce1LieapujLG05AuUPImjCUQ1AjljBHPl7C4wv+kJXjPsluHPy+COat\nXK1o3qAOlt51Mi7NnuT2iZuY1dFkoG6q8jHiiLHr3q5ZfXRonmtSdJI/kaYq8tbrn3bjiY7H7RZX\nJok4PaKCQBSHT5rpK3y77pMZtZVAOKZzcxxusdrc0qyh8ADVKiywzGfljeP7gdTPS7LaSLvo2ivA\nofrEvMPcb4rTe+2bcQ1Tcxxe3HGt5hFiNwRpqDpsNK5XK5Qtfq0w1mRkb1/gpb+yT+NwFEOUd62u\nHiPu+sHBL9KMAtmPwydd92mE16480nJx0guXHRFavcYLdufpPdCkvnV4irjwthGSc2a/L/H95/TC\nhB/WKdWV7XVmzHHkuy9H7mg0HkWiqsDm3jYoZEnSdGyxF94Z2T8jLSmK1kl32a2A90tRYQGKxwx1\nz5jF1QM6YUbxpkBl8YMojjw43CbaapKp6kNkA7vfYW60vY6e0rvzeZBDqdyYxnEWwiWlkc4mqnkh\nP0y/+UQ0sliFHwfXDUrGCEUUhwAgmGF6vvuLu+F1RON1L4dU0xW05ogJ55X5BC+qNaiG3U5xJWFq\nze562W2PUJOROY6gcbQrpw92adXQMVKrazV+pzJcjufzAp/SQz0QHRGU9h2w2orXaQ7CaJj2qlNo\nSlPDl6nKYY5jYLeWADLD6EdBai4o0lr9U00GwTUKGXFEgFXjMtHnPhZBEca2tl5HHDee0hUvT//J\n8piVPM9eejg+mrceLRvVxdotu3OOP3NpX9QuKsCtp3bH8V1aYPxcb0EGvLrv2slp9M6PPbAFnhrR\n15MM2bx/9dFYsG6rp3OcFEaSGmkVB4YguOesnujUsgGGP/INAC1cTBBzFh+NOgazf9qcdzlVEVEc\nEZBk+21cEODZbtyuWX38Xvf+yW7/Du/QDCd00Xr4OSEhFFtLP9uUOpuD8qdHm8YWe7QoYvHYpRpq\n3yPW/H6V3bUNcx7o7D7tsKeiMvX9/CP2y67cV7ld92nkadfD6oSYqgLCeCE6OGyiZPfK1VPcKhIA\nWjbUguo1qefPo8q9UUyWklNWuk6NpHJdGt68quzzxnUl06aqTAkGdmuFm07p5qvMG0/uhoHdWmJA\n15Z5ShcPCRpoVQtkxBEQdYoK8fSIPujZNtc9162XNvm647CyZIdSPVcN6IT2zevjlIPz29gmu1GN\n68Ua2K0VSrblmp3cUAurrudVLDOokCNJ5akRfbC9tNzXue2a1c/b7JaNt+n5POvyG4VUsEQUR4Cc\n2K2Vr/PaNKmHNk3UtsasVViA4b3b+qoHcFdiUXm3GC/yUyP6RFCXWr5KP6aqBMeDcrqXcclmV2+s\n41ybC/XP4QdjRvHGiIWpGojiqKHkTo7H3cxlYl4lbn1cKViV7aHaRRZWWj+T4w654zb6xV2/KlFM\nkvt5us8/Yr/c+RABgMxx2HL/OYfg6gGdAi0zYW1zBmPOOBiDureyNLUFxTOXBGfq6Nq6IU42u/8q\nuOgazL1tEGb930k5+fzMccQ+rFCkjq4okySuubNy1mHaKLpve2/eTr/JCl1i8NdBB2bV5VE4wRFR\nHDYM79028FWaYfaqVGPe2L1AnVs1xBMX97HuiQfECaaJ1Xzf41qFBXj0wsPSCVaT4zbnNq5XyzLy\nb2Wl+g6ABlXBVDW0Z2ssvvPkjGNJa0gPadcExWOGYn8H5xIrHjrPerOyqwZ09ixD0kbdSUZMVdWE\nkSd0wsgTgh0hxUkQL7HflePeFgAmF8OrKkkyWl3aKMKgiFIIFlEcQiL584mdsbxku7rDQQA9f4et\nLezLTnCDlI72m5YxSeImSBTBI6I4IiBJL2tScLsmHZrvhfFXHZ1XHV4XXqrsnJdNEm+tIT+nQqhY\n5EmQ5HG/H1XFiSBJyByHUO3xvOd4FdnIyY6cNTomIeMOJmilsOKWSfCOKI4aRhIbukAIZOW4fQ/d\njqrQEFr9nCQ8B0mQAUjmqDHpiOIQYiGKuYHUynHFutILAKv2iCNlqkolxCVJ1SBher5KIIojQuQB\nDZEAGkc/GzlZkRRlYmV6i/sZVNkxUUg+ojhqKEFOjvbvtDfuOL1HYOUFhV9TVb4jjqSYqpzCxCdB\ntxnRjD0tuBQSgXhVRUiSXo8ge3kvXt4vsLLCwPvkuIeyE3VXM/G1Ej5CHjyvN0q2laJWyDtHqpLM\nq5RMknHHaggJ6YjWGPJ1x23ewD10veWCtoS1QGZ5Ytv/XMcsS91ahWjXrH58wgi+EcVRQ0lyTzkw\nUnMWHjdy0vN/fO1x/qr12TYbe60EBTtM9id54aKQfMRUJVR7vIZVN0xVTep526HQd8UAFvxjcGgm\nJS+mt7iJQ1RDiYpFQB1RHBFQhd7bUHj8osPQunHdyOv12hD42cipqIDwp+MPwCOfLbcQwFmC23/T\nPRXUr37t4F/FMw9riyU/b8NfTcE642gcHzqvN8Z9vVLfn9v52o4+uSsKCwin2US9NZe5e0+FY56X\nLj8C36/Z4i7fub3x9FcrcLDfLXprIKI4ahiFeqMY5Raggw/Kb7dCvxy0r7YfdJd9Girl79a6UcZ/\nFf1BRLhhSFc88tly9N7PW0j6S/p38JTfK3VrFWDMmT0z0mrrE9EndG2J9+auDbV+g98csi9aNKyD\nc5+YhkPaOjfOezeokyOzXZluHNWpOY7q1Nw1335718ffhyXPKzDJiOKIgOGHtsH/pv2UemnjpKiw\nAF/97QS0CNieHhcPntcbf355tuWxYb3a4OA2jdGxRQOlsob02AefXneccn4zU28cgMbZpq2Y5hHI\nYVPWurUKU/c/KsUBAP067o1PrzsOHZp7C5suJBNRHBHw99N64IYhXUPd68ILbZtWH08Wtwllr0rA\nnN+Lyap1Y7Wtf5NAXPffj0IWkokojggoLCA0qpvnRGs14dlL++LNWWviFiNURp7QCUs3bMdpPd3N\nKUnnruHWJpw7Tu+B9Vt2RSyNkBREcQiRcnyXlji+S3DzK11aafMXvz+mY2Bl5su+TerhtSuPjK3+\nQ/dvgmkrNgbipXXBEftbpl/UzzpdqBmI4hCqNE33qo3iMUPjFiNRPDWiL4p/2ZGYFdlC9UOeLEGo\nZjSoU4Qe4loqhIgoDkEQBMETojgEQRAET4jiEAQXzj9iv7hFEALi8PbN4hahWhDa5DgRjQNwKoAN\nzNxDT+sF4DEAdQGUA/gTM08nzWH+PwBOAbATwCXMPEs/ZwSAW/Ri72Tm58KSWRCyWXrXyanV9kLV\nZuYtA7FXHfEHCoIwRxzPAhiSlXY3gL8zcy8At+rfAeBkAJ31vysAPAoARNQMwG0AjgBwOIDbiKhp\niDILQga1CgtQUJWiBAq27N2gDurWKoxbjGpBaOqXmb8govbZyQAa6Z8bAzBiHgwD8DxrmwVMI6Im\nRNQawPEAJjHzRgAgoknQlNHLYcktCEIyeXdkfxT/uiNuMQREv45jFICJRHQvtNHOUXp6GwCrTPlW\n62l26YIg1DAOadcEh7TzFkhSCIeoJ8f/COBaZm4H4FoAT+vpVrYAdkjPgYiuIKIZRDSjpKQkEGEF\nQRCEXKJWHCMAvKV/fh3avAWgjSTamfK1hWbGskvPgZmfYOY+zNynRYsWgQotCIIgpIlacawFYOzH\nOQDAUv3zeAAXk0Y/AFuYeR2AiQAGEVFTfVJ8kJ4mCEKePHbhofj9MeHuCSJUT8J0x30Z2uR2cyJa\nDc076vcA/kNERQB2Q/OgAoAPoLniLoPmjnspADDzRiK6A8B3er5/GBPlgiDkx5AerTGkR+u4xRCq\nIGF6VZ1nc+gwi7wMYKRNOeMAjAtQtBrJlL8eX+O3sBUEIRhkNUwNQXZeEwQhKCTkiCAIguAJURyC\nIAiCJ0RxCIIgCJ4QxSEIgiB4QhSHIAiC4AlRHIIgCIInRHEIgiAInhDFIQiCIHhCFIcgCILgCVEc\ngiAIgidEcQiCIAiekFhVgiDk8OTFfaDFHhWEXERxCIKQw0ndW8UtgpBgxFQlCIIgeEIUhyAIguAJ\nURyCIAiCJ0RxCIIgCJ4QxSEIgiB4QhSHIAiC4AlRHIIgCIInRHEIgiAInqDquDqUiEoA/JhHEc0B\n/BKQOEEicnlD5PKGyOWN6ijX/szcwi1TtVQc+UJEM5i5T9xyZCNyeUPk8obI5Y2aLJeYqgRBEARP\niOIQBEEQPCGKw5on4hbABpHLGyKXN0Qub9RYuWSOQxAEQfCEjDgEQRAET4jiMEFEQ4hoMREtI6LR\nEdfdjoimENFCIppPRNfo6bcT0RoimqP/nWI650Zd1sVENDhE2YqJ6Ae9/hl6WjMimkRES/X/TfV0\nIqIHdbm+J6JDQ5Kpi+mazCGirUQ0Ko7rRUTjiGgDEc0zpXm+PkQ0Qs+/lIhGhCTXPUS0SK/7bSJq\noqe3J6Jdpuv2mOmcw/T7v0yXnUKQy/N9C/p9tZHrVZNMxUQ0R0+P8nrZtQ3xPWPMLH+aua4QwHIA\nHQHUBjAXQPcI628N4FD9c0MASwB0B3A7gL9a5O+uy1gHQAdd9sKQZCsG0Dwr7W4Ao/XPowH8W/98\nCoAPARCAfgC+jejerQewfxzXC8CxAA4FMM/v9QHQDMAK/X9T/XPTEOQaBKBI//xvk1ztzfmyypkO\n4Ehd5g8BnByCXJ7uW9kFPYgAAAZfSURBVBjvq5VcWcfvA3BrDNfLrm2I7RmTEUeawwEsY+YVzFwG\n4BUAw6KqnJnXMfMs/fM2AAsBtHE4ZRiAV5i5lJlXAlgG7TdExTAAz+mfnwNwuin9edaYBqAJEbUO\nWZYTASxnZqdFn6FdL2b+AsBGi/q8XJ/BACYx80Zm3gRgEoAhQcvFzB8zc7n+dRqAtk5l6LI1Yuap\nrLU+z5t+S2ByOWB33wJ/X53k0kcNvwXwslMZIV0vu7YhtmdMFEeaNgBWmb6vhnPDHRpE1B5AbwDf\n6klX6UPOccZwFNHKywA+JqKZRHSFntaKmdcB2oMNoGUMchmci8wXOu7rBXi/PnFct99B65kadCCi\n2UT0OREdo6e10WWJQi4v9y3q63UMgJ+ZeakpLfLrldU2xPaMieJIY2WHjNzljIgaAHgTwChm3grg\nUQAHAOgFYB204TIQrbz9mflQACcDGElExzrkjfQ6ElFtAKcBeF1PSsL1csJOjqiv280AygG8qCet\nA7AfM/cG8BcALxFRowjl8nrfor6f5yGzcxL59bJoG2yz2sgQmGyiONKsBtDO9L0tgLVRCkBEtaA9\nGC8y81sAwMw/M3MFM1cCeBJp80pk8jLzWv3/BgBv6zL8bJig9P8bopZL52QAs5j5Z13G2K+Xjtfr\nE5l8+qToqQAu0M0p0E1Bv+qfZ0KbPzhQl8tszgpFLh/3LcrrVQTgDACvmuSN9HpZtQ2I8RkTxZHm\nOwCdiaiD3os9F8D4qCrXbahPA1jIzGNN6eb5geEADI+P8QDOJaI6RNQBQGdok3JBy7UXETU0PkOb\nXJ2n1294ZYwA8K5Jrot1z45+ALYYw+mQyOgJxn29THi9PhMBDCKiprqZZpCeFihENATA3wCcxsw7\nTektiKhQ/9wR2vVZocu2jYj66c/oxabfEqRcXu9blO/rQACLmDllgoryetm1DYjzGctntr+6/UHz\nRlgCrfdwc8R1Hw1t2Pg9gDn63ykAXgDwg54+HkBr0zk367IuRp6eGw5ydYTmsTIXwHzjugDYG8An\nAJbq/5vp6QTgYV2uHwD0CfGa1QfwK4DGprTIrxc0xbUOwB5ovbrL/FwfaHMOy/S/S0OSaxk0O7fx\njD2m5z1Tv79zAcwC8BtTOX2gNeTLAfwX+sLhgOXyfN+Cfl+t5NLTnwXwh6y8UV4vu7YhtmdMVo4L\ngiAInhBTlSAIguAJURyCIAiCJ0RxCIIgCJ4QxSEIgiB4QhSHIAiC4AlRHIIAgIhaEdFLRLRCD60y\nlYiGxyTL8UR0lOn7H4jo4jhkEQQriuIWQBDiRl9g9Q6A55j5fD1tf2ihTMKqs4jTwQazOR7AdgDf\nAAAzP2aTTxBiQdZxCDUeIjoRWrjs4yyOFQIYA60xrwPgYWZ+nIiOhxYK/BcAPQDMBHAhMzMRHQZg\nLIAG+vFLmHkdEX0GTRn0h7bIbQmAW6CFBf8VwAUA6kGLWlsBoATA1dCi/25n5nuJqBeAx6AtflwO\n4HfMvEkv+1sAJwBoAm3x2pfBXSVBSCOmKkEADoK2+teKy6CFbOgLoC+A3+uhLwAtSukoaHsjdATQ\nX48p9BCAs5j5MADjANxlKq8JMx/HzPcB+ApAP9YC5b0C4AZmLoamGO5n5l4Wjf/zAP7GzD2hrQq+\nzXSsiJkP12W6DYIQEmKqEoQsiOhhaGEeygD8CKAnEZ2lH24MLS5RGYDprMcvIm1nuPYANkMbgUzS\nLGAohBbGwuBV0+e2AF7V4zTVBrDSRa7G0BTP53rSc0hHBQYAI/jdTF0WQQgFURyCoMUcOtP4wswj\niag5gBkAfgJwNTNnBIPTTVWlpqQKaO8TAZjPzEfa1LXD9PkhAGOZebzJ9JUPhjyGLIIQCmKqEgTg\nUwB1ieiPprT6+v+JAP6om6BARAfqUYLtWAygBREdqeevRUQH2eRtDGCN/tm8//M2aFuEZsDMWwBs\nMm0adBGAz7PzCULYSK9EqPHoE9qnA7ifiG6ANim9A1r48dehmX1m6d5XJXDYCpSZy3Sz1oO6aakI\nwAPQRjXZ3A7gdSJaA21C3Jg7eQ/AG0Q0DNrkuJkRAB4jovrQ9oy+1PsvFoT8EK8qQRAEwRNiqhIE\nQRA8IYpDEARB8IQoDkEQBMETojgEQRAET4jiEARBEDwhikMQBEHwhCgOQRAEwROiOARBEARP/D+C\nNgKk5gDCsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb44dd974e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rank_,pop=genetic_algorithm(City_List=cityList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis=[]\n",
    "y_axis=[]\n",
    "for i in cityList:\n",
    "    x_axis.append(i[0])\n",
    "    y_axis.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb445e684e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_axis,y_axis)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}