GA_CW2.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install optproblems"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qk7-LeTZjC2",
        "outputId": "9456b542-6677-4d02-9a66-34a64c1a70e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting optproblems\n",
            "  Downloading optproblems-1.3.tar.gz (2.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 4.9 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: optproblems\n",
            "  Building wheel for optproblems (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for optproblems: filename=optproblems-1.3-py3-none-any.whl size=2040688 sha256=17c3d046c2f6152a93354236e2f07538e8788dbb79fd774a37a68d89c0ccafae\n",
            "  Stored in directory: /root/.cache/pip/wheels/3a/8d/1b/762402c6cbdfd012cf3622d55b6460440d90dafe59dad0c013\n",
            "Successfully built optproblems\n",
            "Installing collected packages: optproblems\n",
            "Successfully installed optproblems-1.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from optproblems import cec2005\n",
        "from optproblems import Individual\n",
        "from datetime import datetime\n",
        "from statistics import mean "
      ],
      "metadata": {
        "id": "jlu0Kw0Md_Pe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Solution:\n",
        "  def __init__(self,individual,score=0):\n",
        "    self.individual = individual\n",
        "    self.score = score\n",
        "  def __str__(self) -> str:\n",
        "     return 'SCORE ' + str(self.score) +' | INDIVIDUAL'+ str(self.individual)\n"
      ],
      "metadata": {
        "id": "2NsdHayXnuRl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Problem:\n",
        "  def __init__(self,f):\n",
        "    self.f = f\n",
        "  \n",
        "  def evaluate(self,sol):\n",
        "      func =  self.f(len(sol))\n",
        "      solution=Individual(sol)\n",
        "      func.evaluate(solution)\n",
        "      return solution.objective_values\n",
        "\n",
        "  def optimal(self,length):\n",
        "    func = self.f(length)\n",
        "    solutions = func.get_optimal_solutions()\n",
        "    for sol in solutions:\n",
        "      func.evaluate(sol) # return single solution\n",
        "      return sol.objective_values\n",
        "    return 0"
      ],
      "metadata": {
        "id": "Qe-3EFpcEo6m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GA:\n",
        "\n",
        "  @staticmethod\n",
        "  def one_point_crossover(p1, p2): ## cross over parents\n",
        "    a1 = p1.individual[:]\n",
        "    b1 = p2.individual[:]\n",
        "    pick = np.random.randint(0,len(p1.individual)) \n",
        "    crossed1 = np.concatenate((b1[:pick],a1[pick:]))\n",
        "    crossed2 = np.concatenate((a1[:pick],b1[pick:]))\n",
        "    return crossed1,crossed2\n",
        "\n",
        "  @staticmethod\n",
        "  def tournament_selection(population,k=3): ## tournament selection \n",
        "    selected = np.random.randint(len(population))\n",
        "    for index in np.random.randint(0, len(population), k-1):\n",
        "      if population[index].score < population[selected].score:\n",
        "        selected = index\n",
        "    return population[selected]\n",
        "\n",
        "\n",
        "  @staticmethod\n",
        "  def single_gene_random_mutation(vector_length,individual,bounds): ## single gene mutation, picking random gen and giving it a random value\n",
        "    if(len(bounds) != 2):\n",
        "      raise Exception('Input range should be an array/tuple of length 2')\n",
        "    rand_pick_gene = np.random.randint(0,vector_length)\n",
        "    individual.individual[rand_pick_gene] = np.random.uniform(bounds[0],bounds[1])\n",
        "\n",
        "  \n",
        "\n",
        "  @staticmethod\n",
        "  def fitness(objFunc,solution,best_solution): ## simple fitness function, picking the best score (lower is better)\n",
        "    solution.score=objFunc.evaluate(solution.individual) \n",
        "    if solution.score < best_solution.score:\n",
        "        best_solution = solution\n",
        "    return best_solution\n",
        "\n",
        "  \n",
        "  @staticmethod\n",
        "  def termination(objFunc,solution):\n",
        "    if((objFunc.optimal(len(solution.individual)) + 5) > solution.score): ## termination state when optimal solution is reached with an error of 5\n",
        "      return True\n",
        "    return False\n",
        "\n",
        "\n",
        "  '''\n",
        "  GA algorithm that initializes a population, iterating over them while doing a fitness assessment, selection and breeding.\n",
        "  -\tSelection -> Selection function [tournament selection is used following coursework specification]\n",
        "  -\tMutation -> Mutation function [single gene random mutation is used following coursework specification]\n",
        "  -\tCrossover -> Cross over function [one point crossover is used following coursework specification]\n",
        "  -\tFitness -> Fitness function a simple conditional picking the best score (using evaluate method from Problem class) \n",
        "  -\tTermination -> Termination function [Compare optimal solution with the given solution (with a small error added)]\n",
        "  -\tObjective function -> a function from the test problems (f1,f2,f3…)\n",
        "  -\tn_gen -> number of generations\n",
        "  -\tpop_size -> Length of the population \n",
        "  -\tBounds -> bounds of the solution (Capped depending on the test functions spec)\n",
        "  -\tVector size -> The vector size of the solution\n",
        "  -\tElite rate -> How much to keep from the parents \n",
        "\n",
        "  Elitism is used to inject fit parents to next generation (Exploitative Variation)\n",
        "  '''\n",
        "  @staticmethod\n",
        "  def Evolve(objFunc,fitness,selection,termination,crossover,mutation,n_gen,pop_size,bounds,vector_size=10,elite_rate=0.5):\n",
        "    if(len(bounds) != 2):\n",
        "      raise Exception('Input range of bounds should be an array/tuple of length 2')\n",
        "    if(pop_size % 2 != 0):## keep population size divisible by 2\n",
        "      pop_size+=1 \n",
        "    population=  [Solution(individual) for individual in np.random.uniform(low=bounds[0],high=bounds[1],size=(np.abs(pop_size),np.abs(vector_size)))] ## initialize population\n",
        "    np.random.shuffle(population)\n",
        "    best_solution = population[np.random.randint(0,len(population))]\n",
        "    history = []\n",
        "    for gen in range(n_gen):\n",
        "      for solution in population: \n",
        "        best_solution = fitness(objFunc,solution,best_solution)\n",
        "        history.append(best_solution.score)\n",
        "        if termination(objFunc,best_solution):\n",
        "          print('Terminated -> reached termination state')\n",
        "          return best_solution,history\n",
        "      selected_parents = [selection(population) for _ in range(len(population))] ## Selection\n",
        "      children = []\n",
        "      for mate in range(0, len(population), 2):\n",
        "        p1, p2 = selected_parents[mate], selected_parents[mate+1]\n",
        "        reproduce = crossover(p1,p2) # cross over\n",
        "        for gene in reproduce:\n",
        "          child= Solution(gene)\n",
        "          mutation(vector_size,child,bounds) ## mutation\n",
        "          children.append(child)\n",
        "\n",
        "      children.sort(key=lambda individual: individual.score, reverse=False) ## sort fittest children by score\n",
        "      population.sort(key=lambda individual: individual.score, reverse=False) ## sort fittest parents by score\n",
        "      elites_rate = int(len(population)* np.abs(elite_rate))  ## Elitism, keep percentage of top parents for next generation  \n",
        "      population[-elites_rate:] = children[:elites_rate if elites_rate > 0 else len(children)]\n",
        "    return best_solution,history"
      ],
      "metadata": {
        "id": "beMMuPMM_HQ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation & Experiments"
      ],
      "metadata": {
        "id": "WNu5K5cl2PbM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1 = Problem(cec2005.F1)\n",
        "f2 = Problem(cec2005.F2)\n",
        "f3 = Problem(cec2005.F3)\n",
        "f4 = Problem(cec2005.F4)\n",
        "f5 = Problem(cec2005.F5)\n",
        "f6 = Problem(cec2005.F6)\n",
        "f8 = Problem(cec2005.F8)"
      ],
      "metadata": {
        "id": "ekuTNzqxFQHU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,GENERATION_ITER,POPULATION_LENGTH,BOUNDS,ELITE_RATE,runs=50):\n",
        "  ## Evaluation \n",
        "  evaluation = {\n",
        "      'accuracies': [],\n",
        "      'timedRuns': [],\n",
        "      'runs': [],\n",
        "      'pop_length': POPULATION_LENGTH,\n",
        "      'n_gen': GENERATION_ITER\n",
        "  }\n",
        "  for run in range(runs):\n",
        "    print('RUN ',run+1)\n",
        "    start_time = datetime.now()\n",
        "    solution,data = GA.Evolve(objFunc=OBJECTIVE_FUNC,\n",
        "                        fitness=GA.fitness,\n",
        "                        selection=GA.tournament_selection,\n",
        "                        crossover=GA.one_point_crossover,\n",
        "                        mutation=GA.single_gene_random_mutation,\n",
        "                        n_gen=GENERATION_ITER,\n",
        "                        pop_size=POPULATION_LENGTH,\n",
        "                        vector_size=VECTOR_LENGTH,\n",
        "                        termination=GA.termination,\n",
        "                        bounds=BOUNDS,\n",
        "                        elite_rate=ELITE_RATE)\n",
        "    delta_time = datetime.now() - start_time \n",
        "    timeTaken = delta_time.total_seconds() * 1000\n",
        "    accuracy = (solution.score / OBJECTIVE_FUNC.optimal(VECTOR_LENGTH)) * 100\n",
        "\n",
        "    evaluation['timedRuns'].append(timeTaken)\n",
        "    evaluation['accuracies'].append(accuracy)\n",
        "    evaluation['runs'].append(data)\n",
        "\n",
        "\n",
        "    print('Time taken->',timeTaken, 'ms')\n",
        "    print('Fittest individual->',solution)\n",
        "    print('Accuracy ->',accuracy,'%')\n",
        "    print('#####################################')\n",
        "  return evaluation\n"
      ],
      "metadata": {
        "id": "vnw7YgA2c1jk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Experiments parameetrs \n",
        "experiment_runs = 25"
      ],
      "metadata": {
        "id": "of0NUDgxDnKf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment 1"
      ],
      "metadata": {
        "id": "8wsnFgbH2Tl_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "CH3SYUYB2Zk4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F1\n",
        "## Hyperparameters\n",
        "VECTOR_LENGTH = 10\n",
        "GENERATION_ITER = 1000\n",
        "POPULATION_LENGTH = 10\n",
        "BOUNDS = [-100,100]\n",
        "ELITE_RATE = 0.5\n",
        "OBJECTIVE_FUNC=f1\n",
        "exp_f1 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,GENERATION_ITER,POPULATION_LENGTH,BOUNDS,ELITE_RATE,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGF5FwxICNLC",
        "outputId": "48c03ff1-7b95-4a37-b95b-94ed630fe056"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUN  1\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 1664.45 ms\n",
            "Fittest individual-> SCORE -445.55343119463316 | INDIVIDUAL[-39.61716627  58.34842098 -47.50267532 -74.31851646 -17.65774653\n",
            " -80.53382952 -10.07007929  24.39401809  88.98670399   9.80367694]\n",
            "Accuracy -> 99.01187359880737 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 832.898 ms\n",
            "Fittest individual-> SCORE -445.03145913074115 | INDIVIDUAL[-40.39987755  59.43606594 -47.44404656 -74.36123453 -15.88018211\n",
            " -79.69441414 -10.49271473  25.28816296  90.52780844   9.08962718]\n",
            "Accuracy -> 98.89587980683136 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 569.431 ms\n",
            "Fittest individual-> SCORE -445.26800279049957 | INDIVIDUAL[-38.87635779  59.70895393 -45.2831921  -73.58617172 -16.30746282\n",
            " -81.08960889 -10.38434865  25.43535936  89.4755334    8.25141028]\n",
            "Accuracy -> 98.94844506455546 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 323.668 ms\n",
            "Fittest individual-> SCORE -446.65318247440973 | INDIVIDUAL[-39.94732214  59.56322357 -47.19856077 -75.39897746 -16.8886346\n",
            " -80.02788331 -10.38780454  24.47357735  90.56372217   8.81517484]\n",
            "Accuracy -> 99.25626277209105 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 373.293 ms\n",
            "Fittest individual-> SCORE -445.48721076360835 | INDIVIDUAL[-39.66837245  58.05470679 -47.15186519 -74.36947862 -16.27987675\n",
            " -81.06455029 -10.83629305  23.80784899  88.98053535   8.64429888]\n",
            "Accuracy -> 98.99715794746852 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 627.6610000000001 ms\n",
            "Fittest individual-> SCORE -445.08401460886626 | INDIVIDUAL[-40.117223    59.12210688 -47.40797576 -74.54640797 -18.30827356\n",
            " -81.03125111 -10.37599372  24.61166052  89.95016298   9.68431369]\n",
            "Accuracy -> 98.90755880197027 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 674.909 ms\n",
            "Fittest individual-> SCORE -445.82480068520636 | INDIVIDUAL[-39.64800878  58.54178331 -46.6907123  -74.62209413 -16.73749954\n",
            " -81.59937391 -10.74534539  23.38455471  89.80098495   9.49111044]\n",
            "Accuracy -> 99.07217793004585 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 760.651 ms\n",
            "Fittest individual-> SCORE -445.08007936509 | INDIVIDUAL[-38.42146728  59.16177589 -45.63415369 -75.69298165 -16.96879011\n",
            " -80.97740384 -12.07652527  24.73185678  89.8769032    9.27464919]\n",
            "Accuracy -> 98.90668430335333 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 1543.498 ms\n",
            "Fittest individual-> SCORE -447.0222152108031 | INDIVIDUAL[-39.54829097  59.25184806 -46.2293682  -73.65527469 -15.9351243\n",
            " -80.34126253  -9.97977858  24.9220757   90.50665675   9.53947694]\n",
            "Accuracy -> 99.33827004684514 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 1232.44 ms\n",
            "Fittest individual-> SCORE -445.8961412438531 | INDIVIDUAL[-40.24350138  58.73013265 -46.48851862 -75.09231118 -17.45235484\n",
            " -79.41737219 -10.67463483  23.89642475  89.84086151   9.4750915 ]\n",
            "Accuracy -> 99.08803138752292 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 1413.479 ms\n",
            "Fittest individual-> SCORE -446.3923471830451 | INDIVIDUAL[-39.76197834  58.49325486 -46.93707309 -74.38524576 -15.64487164\n",
            " -79.69916598 -11.18485402  24.62281029  89.33753799   8.96097321]\n",
            "Accuracy -> 99.19829937401002 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 2142.864 ms\n",
            "Fittest individual-> SCORE -445.11928452055105 | INDIVIDUAL[-39.15350558  59.76234623 -46.37214431 -75.25378307 -15.24834686\n",
            " -80.68841895 -10.0203667   25.18579918  89.21633965   9.8580902 ]\n",
            "Accuracy -> 98.91539656012246 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 1314.268 ms\n",
            "Fittest individual-> SCORE -445.172822538111 | INDIVIDUAL[-40.48413751  60.09161864 -45.78291398 -74.87439399 -16.8550178\n",
            " -79.83231024 -10.91958373  25.82057874  90.3880379    8.88947233]\n",
            "Accuracy -> 98.927293897358 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 1968.7939999999999 ms\n",
            "Fittest individual-> SCORE -446.2517209609835 | INDIVIDUAL[-39.3354947   58.74683136 -46.16281705 -75.25415099 -16.48896967\n",
            " -80.75020752 -11.48003736  25.87913405  88.82836289   8.36165721]\n",
            "Accuracy -> 99.16704910244077 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 1664.3190000000002 ms\n",
            "Fittest individual-> SCORE -445.2305819882699 | INDIVIDUAL[-38.4565101   58.07142218 -45.74948641 -73.55496822 -16.5839827\n",
            " -81.30713938 -10.38391118  25.99085566  89.80508114   8.7900827 ]\n",
            "Accuracy -> 98.94012933072663 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 1782.443 ms\n",
            "Fittest individual-> SCORE -444.9288439393531 | INDIVIDUAL[-39.63831871  58.14707922 -45.04338197 -74.03235206 -16.47914676\n",
            " -79.64937693 -10.75556648  25.14083375  89.5734901    7.95021141]\n",
            "Accuracy -> 98.87307643096736 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 749.64 ms\n",
            "Fittest individual-> SCORE -446.97065176340385 | INDIVIDUAL[-37.86502367  58.94844433 -46.74830252 -74.49255365 -16.67191089\n",
            " -79.78995212 -10.67797981  24.64266793  89.67488294   9.07746035]\n",
            "Accuracy -> 99.32681150297863 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 654.671 ms\n",
            "Fittest individual-> SCORE -446.6938306250703 | INDIVIDUAL[-39.73959925  59.98441241 -45.95419125 -74.66721743 -16.66758246\n",
            " -79.81215935 -11.5088272   24.58895753  90.27178705   9.40799796]\n",
            "Accuracy -> 99.26529569446006 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 426.066 ms\n",
            "Fittest individual-> SCORE -445.07031925406284 | INDIVIDUAL[-39.31058245  59.3554126  -47.78116621 -74.60615996 -17.09921611\n",
            " -79.86502131 -11.9341387   24.72415134  89.86863077   8.68422957]\n",
            "Accuracy -> 98.90451538979175 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 705.437 ms\n",
            "Fittest individual-> SCORE -445.878786931067 | INDIVIDUAL[-39.68418121  59.36557039 -46.11327871 -74.86397784 -15.54986301\n",
            " -81.73259099 -10.2947228   24.77056533  89.86625002   9.86867626]\n",
            "Accuracy -> 99.08417487357045 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 724.8599999999999 ms\n",
            "Fittest individual-> SCORE -445.70235643012325 | INDIVIDUAL[-38.4335758   59.09187617 -46.63635741 -74.23458054 -17.23152824\n",
            " -79.17998731 -10.65915014  24.07382986  89.25004112   9.24283197]\n",
            "Accuracy -> 99.04496809558295 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 457.024 ms\n",
            "Fittest individual-> SCORE -447.543498552462 | INDIVIDUAL[-40.00714441  58.01228167 -45.55437831 -74.52475554 -16.7878368\n",
            " -80.36770235 -10.21288543  24.7761945   89.36220803   9.48491126]\n",
            "Accuracy -> 99.454110789436 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 609.637 ms\n",
            "Fittest individual-> SCORE -446.0237111651661 | INDIVIDUAL[-39.42407652  58.67973715 -47.37330076 -74.98957653 -17.52630183\n",
            " -80.97066035 -10.98730041  25.35745178  88.98621771  10.08902019]\n",
            "Accuracy -> 99.1163802589258 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 482.13 ms\n",
            "Fittest individual-> SCORE -445.4305052815535 | INDIVIDUAL[-39.90183385  60.14847911 -46.35433508 -73.56135885 -16.67118546\n",
            " -80.18522742 -10.76596449  25.80976264  90.17076059   8.41808954]\n",
            "Accuracy -> 98.98455672923411 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 559.469 ms\n",
            "Fittest individual-> SCORE -445.068832018304 | INDIVIDUAL[-39.21055711  58.30826675 -46.12686563 -74.58581222 -17.29472989\n",
            " -79.86266648 -10.24500832  26.76223552  89.15071559   9.01042394]\n",
            "Accuracy -> 98.90418489295645 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "uhf27CFl2bLv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F4\n",
        "## Hyperparameters\n",
        "VECTOR_LENGTH = 10\n",
        "GENERATION_ITER = 1500\n",
        "POPULATION_LENGTH = 100\n",
        "BOUNDS = [-100,100]\n",
        "ELITE_RATE = 0.5\n",
        "OBJECTIVE_FUNC=f4\n",
        "exp_f4 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,GENERATION_ITER,POPULATION_LENGTH,BOUNDS,ELITE_RATE,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bn2Qcre9DOma",
        "outputId": "373a9f74-0330-4f8b-d008-b89ff8d28c7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUN  1\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 9147.548999999999 ms\n",
            "Fittest individual-> SCORE -445.0198869383215 | INDIVIDUAL[ 34.90526077 -80.94194727 -12.6725404  -82.62994642  84.36581986\n",
            "  45.67114587 -89.32116465 -27.5925573   75.78325796 -39.03854675]\n",
            "Accuracy -> 98.89330820851589 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 17743.718 ms\n",
            "Fittest individual-> SCORE -417.72504250240434 | INDIVIDUAL[ 32.79572365 -76.70818703 -15.91747177 -79.88763009  80.18510365\n",
            "  47.79468949 -89.43158045 -27.40443805  76.10725074 -37.4403713 ]\n",
            "Accuracy -> 92.82778722275651 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 12070.867 ms\n",
            "Fittest individual-> SCORE -439.0952611440473 | INDIVIDUAL[ 34.36003583 -80.31925244 -13.14422938 -80.39096716  80.9873568\n",
            "  48.23165649 -90.75866345 -27.83402635  76.93938025 -39.28717382]\n",
            "Accuracy -> 97.57672469867717 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 11297.008 ms\n",
            "Fittest individual-> SCORE -445.1021669217245 | INDIVIDUAL[ 34.31708144 -80.95204609 -10.47063526 -83.84425541  81.91923936\n",
            "  48.12232472 -90.64088868 -26.86085352  76.28084011 -38.59292337]\n",
            "Accuracy -> 98.91159264927212 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 12105.258 ms\n",
            "Fittest individual-> SCORE -443.6488805201712 | INDIVIDUAL[ 37.47760675 -85.00935807 -11.83255597 -81.97932827  83.185594\n",
            "  46.33971265 -88.97939147 -27.64021664  75.94582357 -38.26962562]\n",
            "Accuracy -> 98.58864011559359 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 12036.55 ms\n",
            "Fittest individual-> SCORE -439.8709754391016 | INDIVIDUAL[ 37.42761769 -86.40380217  -9.74093999 -82.17008738  81.83923165\n",
            "  46.90578748 -89.46920694 -27.27363151  76.75947969 -38.5103964 ]\n",
            "Accuracy -> 97.74910565313368 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 9852.031 ms\n",
            "Fittest individual-> SCORE -445.05794565253757 | INDIVIDUAL[ 35.57239664 -81.59184139 -12.10831308 -82.64765485  81.74912299\n",
            "  48.65320014 -89.92784703 -28.13413508  76.84104166 -40.03979685]\n",
            "Accuracy -> 98.9017657005639 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 4889.663 ms\n",
            "Fittest individual-> SCORE -446.06599824448915 | INDIVIDUAL[ 34.65052949 -82.36726187  -9.48128576 -83.96501744  82.99166966\n",
            "  45.77079568 -87.73653477 -28.09323667  76.16877443 -39.2596365 ]\n",
            "Accuracy -> 99.12577738766426 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 13245.718 ms\n",
            "Fittest individual-> SCORE -435.6450589841915 | INDIVIDUAL[ 33.02009072 -78.38368488 -11.88433179 -84.47029451  84.26866074\n",
            "  46.80095828 -90.41442677 -26.81462784  75.44321511 -39.7736436 ]\n",
            "Accuracy -> 96.81001310759811 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 11928.406 ms\n",
            "Fittest individual-> SCORE -443.1321087142533 | INDIVIDUAL[ 35.6243766  -83.80308025 -10.20580134 -82.76963398  81.87359\n",
            "  49.75323055 -91.20069404 -27.93406557  76.21765281 -39.08997873]\n",
            "Accuracy -> 98.47380193650073 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 11382.859 ms\n",
            "Fittest individual-> SCORE -445.04613730871 | INDIVIDUAL[ 36.31775017 -83.88373221 -11.48027921 -81.58639529  81.44785362\n",
            "  47.87292695 -88.43620787 -28.72198965  76.49200402 -38.50872139]\n",
            "Accuracy -> 98.89914162415778 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 11874.565999999999 ms\n",
            "Fittest individual-> SCORE -444.43172075169235 | INDIVIDUAL[ 35.14129916 -83.79649525  -9.27121808 -84.5791488   83.78617144\n",
            "  48.02205383 -90.11434144 -26.2888579   74.55046656 -38.44048305]\n",
            "Accuracy -> 98.76260461148719 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 14819.511999999999 ms\n",
            "Fittest individual-> SCORE -440.76066796016363 | INDIVIDUAL[ 36.14758987 -83.57494935  -8.7170322  -87.2483168   84.13202408\n",
            "  48.54007396 -89.97194293 -26.64968937  75.17205781 -39.21031979]\n",
            "Accuracy -> 97.94681510225858 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 6708.235000000001 ms\n",
            "Fittest individual-> SCORE -445.4373888468838 | INDIVIDUAL[ 35.32960818 -82.6725849  -11.95632756 -82.76683553  84.16034881\n",
            "  46.5570315  -89.99530242 -26.50025103  76.81698501 -40.81925   ]\n",
            "Accuracy -> 98.98608641041862 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 8939.132000000001 ms\n",
            "Fittest individual-> SCORE -446.41412338377916 | INDIVIDUAL[ 35.3557993  -81.87758056 -11.75717001 -84.03951131  83.79081639\n",
            "  47.04168305 -89.33072376 -26.96506909  76.75950921 -40.41631685]\n",
            "Accuracy -> 99.20313852972869 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 11922.105 ms\n",
            "Fittest individual-> SCORE -431.4954038621203 | INDIVIDUAL[ 33.1491772  -79.03859238 -10.11658134 -84.03801504  82.51628001\n",
            "  45.7462449  -88.01542926 -28.65447426  75.54596517 -39.44012573]\n",
            "Accuracy -> 95.88786752491562 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 3441.557 ms\n",
            "Fittest individual-> SCORE -446.32664447620743 | INDIVIDUAL[ 35.10440101 -81.9201454  -11.10207422 -83.38160246  82.54275505\n",
            "  47.15061875 -90.22101012 -26.62739063  75.45558618 -38.77111242]\n",
            "Accuracy -> 99.18369877249053 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 9383.411 ms\n",
            "Fittest individual-> SCORE -445.36186977908176 | INDIVIDUAL[ 35.55442456 -82.40571016 -12.75356259 -81.11962017  82.40975151\n",
            "  46.81789368 -89.17917837 -27.38950251  76.3056605  -40.21450218]\n",
            "Accuracy -> 98.9693043953515 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 11905.916 ms\n",
            "Fittest individual-> SCORE -439.54573518223634 | INDIVIDUAL[ 37.68296744 -86.15938221  -9.87929023 -83.65612164  84.23073004\n",
            "  46.35288079 -89.16422314 -26.90482852  74.52635802 -39.06345067]\n",
            "Accuracy -> 97.67683004049697 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 11758.988000000001 ms\n",
            "Fittest individual-> SCORE -440.10134048866325 | INDIVIDUAL[ 34.34622724 -79.80518923 -13.33779078 -82.78312328  83.42677906\n",
            "  47.1927702  -88.73421096 -28.06615062  76.850503   -39.65280631]\n",
            "Accuracy -> 97.80029788636962 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 11936.992999999999 ms\n",
            "Fittest individual-> SCORE -426.66749446679347 | INDIVIDUAL[ 33.39704934 -79.82026462 -12.64957213 -79.22210595  79.40368469\n",
            "  48.27512013 -90.76776366 -25.4048439   74.24907241 -37.02454324]\n",
            "Accuracy -> 94.81499877039855 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 11258.113 ms\n",
            "Fittest individual-> SCORE -445.8445513672752 | INDIVIDUAL[ 34.70566353 -82.36623618  -9.53593527 -85.15538086  84.18600842\n",
            "  47.13673044 -90.36326188 -26.31575632  75.79538954 -39.81496026]\n",
            "Accuracy -> 99.0765669705056 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 11149.515 ms\n",
            "Fittest individual-> SCORE -445.1066079127411 | INDIVIDUAL[ 35.93603226 -82.79417326 -12.1916244  -81.57920429  81.34526303\n",
            "  48.70430453 -90.70453262 -26.18447906  75.04328311 -38.1724918 ]\n",
            "Accuracy -> 98.91257953616469 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 8370.373000000001 ms\n",
            "Fittest individual-> SCORE -445.74221833981625 | INDIVIDUAL[ 33.97118491 -80.97825856 -10.50554009 -83.94488435  82.84618843\n",
            "  46.7751568  -88.50165122 -27.42512492  76.30348207 -39.82190687]\n",
            "Accuracy -> 99.05382629773695 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Terminated -> reached termination state\n",
            "Time taken-> 2272.84 ms\n",
            "Fittest individual-> SCORE -445.5118477948184 | INDIVIDUAL[ 35.31124248 -83.50890423  -8.54806267 -84.41144475  81.92944135\n",
            "  47.50106648 -88.80458159 -28.49148217  76.72005642 -39.08992413]\n",
            "Accuracy -> 99.00263284329297 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "gD-6O3qT2cyw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F8\n",
        "## Hyperparameters\n",
        "VECTOR_LENGTH = 10\n",
        "GENERATION_ITER = 100\n",
        "POPULATION_LENGTH = 6\n",
        "BOUNDS = [-32,32]\n",
        "ELITE_RATE = 0.5\n",
        "OBJECTIVE_FUNC = f8\n",
        "exp_f8 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,GENERATION_ITER,POPULATION_LENGTH,BOUNDS,ELITE_RATE,experiment_runs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCM6Tx9MDxmu",
        "outputId": "e915547e-dbe3-4803-ef55-0645d1b81f2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUN  1\n",
            "Time taken-> 80.664 ms\n",
            "Fittest individual-> SCORE -119.07618904632132 | INDIVIDUAL[-14.70805877 -11.59638761   5.65969945   4.98974501 -23.27291015\n",
            "  10.14359681  27.13977182  10.21391442   1.59741365 -27.76494251]\n",
            "Accuracy -> 85.05442074737238 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 83.432 ms\n",
            "Fittest individual-> SCORE -118.98315460080254 | INDIVIDUAL[ 31.95076881 -15.89477172  -1.13719126 -25.39685819  -1.52248436\n",
            "  15.27418007  24.07573753  31.88168458 -19.94935505 -17.92267329]\n",
            "Accuracy -> 84.9879675720018 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 89.408 ms\n",
            "Fittest individual-> SCORE -119.17888136057596 | INDIVIDUAL[ -2.51437673 -30.88947613  -9.85710427 -31.28522891  28.95017467\n",
            "  14.75382308  31.71675535   9.51252311  -8.64685123  22.3118035 ]\n",
            "Accuracy -> 85.12777240041139 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 77.526 ms\n",
            "Fittest individual-> SCORE -119.14729374256946 | INDIVIDUAL[  1.36972809  17.50510941 -12.97683583   1.12526635 -15.29075798\n",
            "  21.90525632  -4.2741608  -13.49262912  -2.72486842 -17.75230763]\n",
            "Accuracy -> 85.10520981612105 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 75.721 ms\n",
            "Fittest individual-> SCORE -119.02107130369356 | INDIVIDUAL[ -4.91224245 -30.79115307  31.06918966  27.83357761  18.33689507\n",
            " -27.71093609 -19.41589907 -17.58369998  17.23631738  16.50436255]\n",
            "Accuracy -> 85.01505093120969 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 97.01 ms\n",
            "Fittest individual-> SCORE -119.2852619772459 | INDIVIDUAL[ 10.31582565   8.64632658 -14.50716898 -18.12178167  -2.90499528\n",
            " -18.97132951 -24.02917488  23.28400805  -3.39412453   8.23465622]\n",
            "Accuracy -> 85.20375855517564 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 76.166 ms\n",
            "Fittest individual-> SCORE -118.96751908037825 | INDIVIDUAL[ -1.43663449 -23.12610413 -18.00259639  12.83691978  18.43439693\n",
            " -26.65044416  16.80811802   7.92434281   0.97660693  -1.83212166]\n",
            "Accuracy -> 84.97679934312731 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 77.488 ms\n",
            "Fittest individual-> SCORE -118.99991346516975 | INDIVIDUAL[-23.82912783  19.55521426  -9.56885784 -19.10213288  15.3236386\n",
            " -11.45366886   3.66511449   5.95020949  29.87169466   1.40962778]\n",
            "Accuracy -> 84.99993818940696 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 112.833 ms\n",
            "Fittest individual-> SCORE -119.02917040141415 | INDIVIDUAL[-19.684862    -7.4300848   25.17535255  24.35813278   2.34373719\n",
            " -28.19005556  17.13086925  -1.14120214 -18.34555446   1.83487345]\n",
            "Accuracy -> 85.0208360010101 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 131.209 ms\n",
            "Fittest individual-> SCORE -119.09791382261764 | INDIVIDUAL[  1.97119988  -6.1647505    5.1891863  -20.25231409  30.2547365\n",
            "  14.60881381 -24.31615008  13.69654216   8.7097125   27.80573632]\n",
            "Accuracy -> 85.06993844472689 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 84.182 ms\n",
            "Fittest individual-> SCORE -119.07763236226954 | INDIVIDUAL[ 13.9382804  -18.02321735  -5.0913133   15.53181611 -28.26737835\n",
            " -20.04679491  27.89729132  -7.64513717 -21.19276044 -28.38018126]\n",
            "Accuracy -> 85.05545168733538 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 73.20500000000001 ms\n",
            "Fittest individual-> SCORE -119.1178924664481 | INDIVIDUAL[ -7.6403925  -22.92617514  19.56912993  12.23388663 -12.66451503\n",
            "  29.74383381  26.08809522  30.37381182  29.61531472  28.06197281]\n",
            "Accuracy -> 85.08420890460579 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 77.11999999999999 ms\n",
            "Fittest individual-> SCORE -118.98064016805196 | INDIVIDUAL[-5.52200741  6.56425494 -8.58263728 -7.90573592  6.00510729 -4.54820413\n",
            " -1.39121797  1.86806846 16.41339204 23.75855731]\n",
            "Accuracy -> 84.98617154860855 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 93.816 ms\n",
            "Fittest individual-> SCORE -119.2959909445738 | INDIVIDUAL[  4.45821255  -7.23538753 -20.29695343 -14.95081378  -4.64777523\n",
            "  17.6642597  -26.53382678 -26.41231637  31.8589374   18.44569347]\n",
            "Accuracy -> 85.211422103267 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 75.501 ms\n",
            "Fittest individual-> SCORE -119.37797314177851 | INDIVIDUAL[  8.55033775  16.67469836  -8.87625363 -14.50796048   7.86087977\n",
            " -25.11044727  -5.34824996  -5.75031002 -14.30054629   8.90361455]\n",
            "Accuracy -> 85.26998081555608 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 74.68299999999999 ms\n",
            "Fittest individual-> SCORE -119.12253764368708 | INDIVIDUAL[  8.31917207 -17.35522038  14.69493007 -13.42005295 -24.40365146\n",
            "  25.14405616  27.26430594  22.24862147 -10.44659426  25.00676795]\n",
            "Accuracy -> 85.08752688834792 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 80.943 ms\n",
            "Fittest individual-> SCORE -119.00616371509307 | INDIVIDUAL[  3.86736557 -19.97529634  31.41363861 -26.26475326   7.16388984\n",
            " -11.74524212  27.75234334   9.53340067  18.19531658  20.19034993]\n",
            "Accuracy -> 85.0044026536379 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 81.931 ms\n",
            "Fittest individual-> SCORE -118.93687020716283 | INDIVIDUAL[-16.57999363  20.95768089   6.78698122   5.23875882  15.04556293\n",
            "   1.40214229  25.36028495   7.04459446  17.03467817  19.36009515]\n",
            "Accuracy -> 84.95490729083059 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 76.346 ms\n",
            "Fittest individual-> SCORE -118.99818710710349 | INDIVIDUAL[ -2.96297439  14.2865035  -13.74815103  15.23536923 -22.95744123\n",
            "  14.87739243 -12.53202028 -11.59803315  -8.07838574  20.24488063]\n",
            "Accuracy -> 84.99870507650249 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 120.7 ms\n",
            "Fittest individual-> SCORE -119.26035223063774 | INDIVIDUAL[ -4.69950175  27.69469681  24.81176641  15.56484312  23.57246231\n",
            " -19.34291127 -23.13605292  19.30001185  16.61999278 -19.4921819 ]\n",
            "Accuracy -> 85.18596587902695 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 80.023 ms\n",
            "Fittest individual-> SCORE -119.14789033788426 | INDIVIDUAL[-26.61758638   0.46968411 -17.04954949  22.69512532  10.41636673\n",
            "  -4.29387705 -27.44427665  18.87698993 -15.81618277  -7.64539825]\n",
            "Accuracy -> 85.10563595563163 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 79.729 ms\n",
            "Fittest individual-> SCORE -119.01253922940646 | INDIVIDUAL[ 14.41227367 -25.69004522 -25.47804295  -0.18339018  13.25845553\n",
            "  30.8004109   -8.38480466  -6.69536185 -24.8135788   15.80576129]\n",
            "Accuracy -> 85.00895659243318 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 88.88300000000001 ms\n",
            "Fittest individual-> SCORE -119.11752583733549 | INDIVIDUAL[ 19.32958357 -26.29407224 -25.96062637   1.09587211  29.78174851\n",
            "  -7.93973813  18.30203485  26.12579723  13.37064911  -0.51287134]\n",
            "Accuracy -> 85.0839470266682 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 82.176 ms\n",
            "Fittest individual-> SCORE -119.06260290716358 | INDIVIDUAL[-30.7676115    3.84725422  18.63160251   4.57272455  -4.53129506\n",
            " -19.4066089  -20.04152055 -17.42446986  -4.58048043 -20.91017325]\n",
            "Accuracy -> 85.04471636225969 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 77.16499999999999 ms\n",
            "Fittest individual-> SCORE -118.9606434220343 | INDIVIDUAL[-26.34582436 -21.62583457  -5.75330731 -21.60114516   2.77210129\n",
            "  30.02423098  28.04492511   2.45647834  16.66396291   6.24591444]\n",
            "Accuracy -> 84.97188815859592 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis"
      ],
      "metadata": {
        "id": "zgOmf9f82qQ6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1, F4 ,F8"
      ],
      "metadata": {
        "id": "IkdlZyZE2s-s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_axis = [i for i in range(experiment_runs)]\n",
        "plt.plot(x_axis, exp_f1['accuracies'])\n",
        "plt.plot(x_axis, exp_f4['accuracies'])\n",
        "plt.plot(x_axis, exp_f8['accuracies'])\n",
        "\n",
        "plt.title('Accuracy vs Runs')\n",
        "plt.xlabel('Runs')\n",
        "plt.ylabel('Accuracy(%)')\n",
        "plt.legend(['F1', 'F4','F8'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haOBOttMD8Ix",
        "outputId": "301ca1db-47e4-4a34-a6b9-852d5ebf2e2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "TiMelcwW2vlW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F1 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f1['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f1['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cvxh5MdijDoi",
        "outputId": "8556fafd-6b65-455d-cd77-2a6bc530c5d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "IpALQBS22wgC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F4 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f4['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f4['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NV3fKt18lel_",
        "outputId": "a98b04c4-e552-41c8-cb4e-b9750f6fd968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "r2iDh_Ll2xze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F8 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp_f8['runs'][0]))]\n",
        "plt.plot(x_axis, exp_f8['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O_Tx0LBjljdt",
        "outputId": "15cbb6d4-da34-4db8-e2b0-e5a1c9bd2e1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_exp_f1 = np.max(exp_f1['accuracies'])\n",
        "print('Best F1 Accuracy->',best_exp_f1)\n",
        "best_exp_f4 = np.max(exp_f4['accuracies'])\n",
        "print('Best F4 Accuracy->',best_exp_f4)\n",
        "best_exp_f8 = np.max(exp_f8['accuracies'])\n",
        "print('Best F8 Accuracy->',best_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYuqVFClcujS",
        "outputId": "144cc138-c822-4dd0-96aa-98c9a403ded2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 Accuracy-> 99.454110789436\n",
            "Best F4 Accuracy-> 99.20313852972869\n",
            "Best F8 Accuracy-> 85.26998081555608\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "worst_exp_f1 = np.min(exp_f1['accuracies'])\n",
        "print('Worst F1 Accuracy->',worst_exp_f1)\n",
        "worst_exp_f4 = np.min(exp_f4['accuracies'])\n",
        "print('Worst F4 Accuracy->',worst_exp_f4)\n",
        "worst_exp_f8 = np.min(exp_f8['accuracies'])\n",
        "print('Worst F8 Accuracy->',worst_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoX1PdIXcvtd",
        "outputId": "9e2f2e4a-fd13-44e3-d971-df709087d424"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Worst F1 Accuracy-> 98.87307643096736\n",
            "Worst F4 Accuracy-> 92.82778722275651\n",
            "Worst F8 Accuracy-> 84.95490729083059\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "median_exp_f1 = np.median(exp_f1['accuracies'])\n",
        "print('median F1 Accuracy->',median_exp_f1)\n",
        "median_exp_f4 = np.median(exp_f4['accuracies'])\n",
        "print('median F4 Accuracy->',median_exp_f4)\n",
        "median_exp_f8 = np.median(exp_f8['accuracies'])\n",
        "print('median F8 Accuracy->',median_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhDAw_ctcxJK",
        "outputId": "97206d24-66cf-4e25-907e-d2f83195cdc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "median F1 Accuracy-> 99.01187359880737\n",
            "median F4 Accuracy-> 98.89330820851589\n",
            "median F8 Accuracy-> 85.05442074737238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp_f1 = mean(exp_f1['accuracies'])\n",
        "accuracy_exp_f1 = mean(exp_f1['accuracies'][0:20])\n",
        "efficiency_exp_f1 = mean(exp_f1['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp_f1)\n",
        "print(\"Accuracy\",accuracy_exp_f1)\n",
        "print(\"Efficiency\",efficiency_exp_f1)"
      ],
      "metadata": {
        "id": "7nSaBfB_Osze",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1aa2a55d-851b-4e15-dd66-1e4f8764c693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 99.06114338328211\n",
            "Accuracy 99.05121919079588\n",
            "Efficiency 970.32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp_f4 = mean(exp_f4['accuracies'])\n",
        "accuracy_exp_f4 = mean(exp_f4['accuracies'][0:20])\n",
        "efficiency_exp_f4 = mean(exp_f4['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp_f4)\n",
        "print(\"Accuracy\",accuracy_exp_f4)\n",
        "print(\"Efficiency\",efficiency_exp_f4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc56b719-4fc5-4128-95ae-e333243e913e",
        "id": "RAgTd17QOz3L"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 98.081396239842\n",
            "Accuracy 98.05871507889755\n",
            "Efficiency 10457.63532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp_f8 = mean(exp_f8['accuracies'])\n",
        "accuracy_exp_f8 = mean(exp_f8['accuracies'][0:20])\n",
        "efficiency_exp_f8 = mean(exp_f8['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp_f8)\n",
        "print(\"Accuracy\",accuracy_exp_f8)\n",
        "print(\"Efficiency\",efficiency_exp_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf5c66e2-75c6-4fec-fac5-c7bda9204720",
        "id": "skOon52TOz3N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 85.06462315775482\n",
            "Accuracy 85.0700217424141\n",
            "Efficiency 85.9144\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment 2"
      ],
      "metadata": {
        "id": "qcGKYUlE20nQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "rSFMVYya4VbA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F1\n",
        "## Hyperparameters\n",
        "VECTOR_LENGTH = 10\n",
        "GENERATION_ITER = 250\n",
        "POPULATION_LENGTH = 10\n",
        "BOUNDS = [-100,100]\n",
        "ELITE_RATE = 0.5\n",
        "OBJECTIVE_FUNC=f1\n",
        "exp2_f1 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,GENERATION_ITER,POPULATION_LENGTH,BOUNDS,ELITE_RATE,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rhcdyte94fn4",
        "outputId": "6fc57f6c-d62e-46a2-f218-a5a1f5f6670d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUN  1\n",
            "Time taken-> 233.89 ms\n",
            "Fittest individual-> SCORE -405.4835652202593 | INDIVIDUAL[-38.16670298  55.5135617  -44.57319601 -73.5835817  -13.63744252\n",
            " -79.90329529 -11.50836376  21.91838559  88.39999124   6.89552459]\n",
            "Accuracy -> 90.1074589378354 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 201.85 ms\n",
            "Fittest individual-> SCORE -386.8700335399885 | INDIVIDUAL[-40.16260126  57.56653088 -45.82003302 -76.08847021 -22.36845901\n",
            " -81.49036708 -11.22379763  22.42227446  93.80679007  11.05391004]\n",
            "Accuracy -> 85.9711185644419 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 220.611 ms\n",
            "Fittest individual-> SCORE -429.03670189193986 | INDIVIDUAL[-36.63745638  57.09034883 -46.22120854 -76.03591007 -17.8674839\n",
            " -80.79847672  -9.84137882  22.49440039  89.35867133   8.41690062]\n",
            "Accuracy -> 95.34148930931997 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 237.154 ms\n",
            "Fittest individual-> SCORE -388.6469357466658 | INDIVIDUAL[-38.87726787  63.56089435 -42.857388   -76.53630767 -13.78557226\n",
            " -77.1852243   -9.88895881  26.64256989  89.39785367   9.26524266]\n",
            "Accuracy -> 86.36598572148128 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 218.351 ms\n",
            "Fittest individual-> SCORE -409.6795576574704 | INDIVIDUAL[-41.97332921  56.30361438 -45.66642598 -74.35601468 -18.82118082\n",
            " -84.21392931  -9.05440211  26.79186865  90.19794171  10.72602763]\n",
            "Accuracy -> 91.03990170166009 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 193.11700000000002 ms\n",
            "Fittest individual-> SCORE -401.69108997891766 | INDIVIDUAL[-40.69860481  59.507978   -44.91919977 -74.78341211 -12.59184719\n",
            " -79.66406668  -9.79780637  26.73710978  92.48331905  12.95843381]\n",
            "Accuracy -> 89.26468666198171 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 220.932 ms\n",
            "Fittest individual-> SCORE -407.5476546753008 | INDIVIDUAL[-39.63330053  58.15296492 -46.71793146 -70.0601318  -13.83027797\n",
            " -78.80847634 -12.98461481  23.27371417  89.50190778   9.2466732 ]\n",
            "Accuracy -> 90.56614548340019 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 229.023 ms\n",
            "Fittest individual-> SCORE -394.3260636271846 | INDIVIDUAL[-40.71000094  59.56480266 -48.19413524 -68.84123504 -15.16575389\n",
            " -77.4801738   -8.61779318  25.02955545  89.75316184   8.90487937]\n",
            "Accuracy -> 87.62801413937437 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 191.163 ms\n",
            "Fittest individual-> SCORE -419.34532784705675 | INDIVIDUAL[-39.69526473  59.24897229 -41.67572105 -73.13000835 -17.37492151\n",
            " -81.40307271 -10.45909345  25.28586206  87.95059277   7.79715796]\n",
            "Accuracy -> 93.18785063267929 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 208.187 ms\n",
            "Fittest individual-> SCORE -329.57554911088994 | INDIVIDUAL[-35.51418966  59.08055347 -43.22619265 -72.2569855  -13.26991165\n",
            " -82.02146742  -4.23909141  19.05553283  89.04709375   8.94310296]\n",
            "Accuracy -> 73.2390109135311 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 207.75199999999998 ms\n",
            "Fittest individual-> SCORE -346.585829415691 | INDIVIDUAL[-38.3974215   58.38810344 -41.49336272 -75.12945696 -17.19649582\n",
            " -86.66806844 -11.08618379  27.71251626  94.80600961   6.16861709]\n",
            "Accuracy -> 77.0190732034869 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 236.733 ms\n",
            "Fittest individual-> SCORE -424.6123254766822 | INDIVIDUAL[-39.85033244  58.60494359 -45.64023918 -75.55305771 -13.82450649\n",
            " -82.15258095  -9.16479827  24.40207859  88.66941378   6.18545887]\n",
            "Accuracy -> 94.35829455037383 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 205.08100000000002 ms\n",
            "Fittest individual-> SCORE -384.64464994358036 | INDIVIDUAL[-38.59656412  61.14784735 -43.19353393 -75.49125386 -16.48925657\n",
            " -81.55677574 -12.32678101  25.52380704  83.82646395   6.15919016]\n",
            "Accuracy -> 85.47658887635119 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 214.841 ms\n",
            "Fittest individual-> SCORE -364.0556783096431 | INDIVIDUAL[-33.08492478  60.98821651 -49.44933438 -73.09204147 -20.56244122\n",
            " -83.23664115 -12.59070872  26.34343446  91.62080404   8.74226636]\n",
            "Accuracy -> 80.90126184658736 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 193.282 ms\n",
            "Fittest individual-> SCORE -398.1903561639271 | INDIVIDUAL[-39.05377757  62.91373085 -45.25365287 -73.07286941 -13.64411947\n",
            " -79.03732877  -7.8052292   26.19149721  86.64585921   8.55553398]\n",
            "Accuracy -> 88.48674581420603 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 212.26 ms\n",
            "Fittest individual-> SCORE -405.3342000837705 | INDIVIDUAL[-36.56520394  58.61356055 -43.6006065  -71.02503042 -19.57020523\n",
            " -81.77438773  -8.10523817  24.95328881  89.96656487   8.07142457]\n",
            "Accuracy -> 90.07426668528234 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 222.869 ms\n",
            "Fittest individual-> SCORE -401.45229246707345 | INDIVIDUAL[-34.70504009  58.99840985 -43.81955317 -74.25105777 -20.05102127\n",
            " -79.24915893 -10.33178645  26.4765594   90.22440367  11.59247739]\n",
            "Accuracy -> 89.21162054823854 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 203.41600000000003 ms\n",
            "Fittest individual-> SCORE -429.9012808926789 | INDIVIDUAL[-39.34952244  58.4526084  -48.6981903  -73.39343052 -16.85906767\n",
            " -81.20165805 -12.27343596  24.06266545  88.07347542  11.45174984]\n",
            "Accuracy -> 95.53361797615088 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 206.758 ms\n",
            "Fittest individual-> SCORE -428.337493271549 | INDIVIDUAL[-39.51254093  59.07064121 -47.95872501 -77.60274173 -15.99749126\n",
            " -81.2759309   -9.09492251  23.80801107  87.70032602  10.03962607]\n",
            "Accuracy -> 95.18610961589978 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 214.446 ms\n",
            "Fittest individual-> SCORE -407.9220249905795 | INDIVIDUAL[-35.49232285  59.24443702 -46.98919408 -77.36853457 -17.2323382\n",
            " -79.45928968  -7.76574065  23.90137568  87.86779352   6.84055008]\n",
            "Accuracy -> 90.64933888679543 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 208.12 ms\n",
            "Fittest individual-> SCORE -424.8372738841335 | INDIVIDUAL[-38.18402718  57.75073804 -46.59836183 -74.46710804 -17.70499127\n",
            " -78.70049926  -7.69717977  27.29099017  87.73882179   9.34600272]\n",
            "Accuracy -> 94.408283085363 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 194.655 ms\n",
            "Fittest individual-> SCORE -426.7082785319751 | INDIVIDUAL[-42.18248256  56.81708201 -47.44131154 -74.63037542 -16.30683443\n",
            " -78.82505051  -8.45637124  25.34811485  90.38918442  10.22938905]\n",
            "Accuracy -> 94.82406189599448 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 217.67600000000002 ms\n",
            "Fittest individual-> SCORE -343.0703527040172 | INDIVIDUAL[-37.65511177  56.09718503 -45.86698137 -76.52290035 -23.99642886\n",
            " -79.29839294 -12.9103417   23.93441719  94.05507263   5.23169664]\n",
            "Accuracy -> 76.23785615644826 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 199.637 ms\n",
            "Fittest individual-> SCORE -405.87102015168654 | INDIVIDUAL[-40.10143323  57.86061209 -46.58557208 -76.39559142 -14.95035401\n",
            " -83.75916163  -9.187275    29.50687177  91.49822432   9.59825978]\n",
            "Accuracy -> 90.19356003370812 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 203.496 ms\n",
            "Fittest individual-> SCORE -414.3135326571417 | INDIVIDUAL[-42.49705361  57.57701523 -43.56693478 -73.04912369 -15.80028416\n",
            " -82.2751598  -11.56606409  25.05789791  91.48246787   6.6667069 ]\n",
            "Accuracy -> 92.06967392380928 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "Nt3YHeeB42wT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F4\n",
        "## Hyperparameters\n",
        "VECTOR_LENGTH = 10\n",
        "GENERATION_ITER = 250\n",
        "POPULATION_LENGTH = 10\n",
        "BOUNDS = [-100,100]\n",
        "ELITE_RATE = 0.5\n",
        "OBJECTIVE_FUNC=f4\n",
        "exp2_f4 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,GENERATION_ITER,POPULATION_LENGTH,BOUNDS,ELITE_RATE,experiment_runs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ek6v_qfe43i0",
        "outputId": "ad8388e5-d5a5-4a3c-e31e-ffaade4c6db4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUN  1\n",
            "Time taken-> 233.67 ms\n",
            "Fittest individual-> SCORE 1387.5736838748478 | INDIVIDUAL[ 22.17675001 -67.79363432 -21.45278462 -58.91894534  90.34396522\n",
            "  14.63112425 -98.36246339   8.48155491  58.10587171 -38.01627406]\n",
            "Accuracy -> -308.349707527744 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 232.12 ms\n",
            "Fittest individual-> SCORE 669.2163621519869 | INDIVIDUAL[ 31.18835702 -63.50160086 -42.10069367 -72.30879481  88.53807168\n",
            "  23.7214281  -67.42605391 -24.48704577  72.34149987 -37.73762149]\n",
            "Accuracy -> -148.71474714488596 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 231.958 ms\n",
            "Fittest individual-> SCORE 4584.084769663237 | INDIVIDUAL[  5.40530208 -31.51483725 -46.33330721 -35.45745048  73.63622437\n",
            "  -9.79062104 -46.5652306  -34.03171096  69.26387955 -12.54142396]\n",
            "Accuracy -> -1018.6855043696082 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 227.27100000000002 ms\n",
            "Fittest individual-> SCORE 354.13871393673605 | INDIVIDUAL[ 23.96969843 -84.01566787   3.70492314 -96.98688968  97.46201577\n",
            "  39.73269337 -69.11341502 -46.18129265  74.78358219 -36.86079595]\n",
            "Accuracy -> -78.69749198594135 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 229.843 ms\n",
            "Fittest individual-> SCORE 1410.7161274477603 | INDIVIDUAL[ 37.02441172 -90.14070609 -30.01957281 -40.07528905  72.18524421\n",
            "  20.42902912 -64.27391622 -39.67283741  74.37321959 -19.04820822]\n",
            "Accuracy -> -313.49247276616893 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 246.659 ms\n",
            "Fittest individual-> SCORE 1131.8396935227363 | INDIVIDUAL[ 25.15614245 -43.53536604 -45.6747997  -61.27581109  62.74339288\n",
            "  49.8518163  -76.91175522 -34.91171194  82.97815556 -43.50785145]\n",
            "Accuracy -> -251.5199318939414 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 223.11 ms\n",
            "Fittest individual-> SCORE 2412.376152672942 | INDIVIDUAL[  4.263575   -22.57870578 -42.83145361 -75.10619028  85.66371729\n",
            "  21.06036589 -70.07485384 -27.8306327   82.2744465  -52.7257875 ]\n",
            "Accuracy -> -536.083589482876 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 225.849 ms\n",
            "Fittest individual-> SCORE 1187.737747087539 | INDIVIDUAL[ 35.81621735 -91.11469784 -16.72979389 -40.53394483  39.46004591\n",
            "  62.04041821 -94.85446605 -16.74614132  71.59677573 -26.42067453]\n",
            "Accuracy -> -263.94172157500867 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 227.522 ms\n",
            "Fittest individual-> SCORE 4080.8904650486184 | INDIVIDUAL[-11.21355477  -3.91762289 -59.04001256 -47.50183812  59.22442521\n",
            "  34.48557061 -74.97012698 -13.66715628  74.22752884 -39.43321638]\n",
            "Accuracy -> -906.8645477885818 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 228.273 ms\n",
            "Fittest individual-> SCORE 422.21121526000707 | INDIVIDUAL[ 19.50155278 -57.13388665 -25.39745106 -68.16251594  78.71324278\n",
            "  28.65839393 -85.33217922 -21.27360076  78.13291268 -40.89856471]\n",
            "Accuracy -> -93.8247145022238 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 234.227 ms\n",
            "Fittest individual-> SCORE 1750.1419238751628 | INDIVIDUAL[ 14.37921462 -78.5944291   17.87906802 -95.78494584  98.0173617\n",
            "  12.09915611 -68.95936596  -9.08574646  55.94209901 -33.30490485]\n",
            "Accuracy -> -388.9204275278139 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 224.42000000000002 ms\n",
            "Fittest individual-> SCORE 6345.353824079178 | INDIVIDUAL[  3.53988819  -0.56536569 -81.08888089 -70.13278779  86.61175383\n",
            "  28.02847473 -93.4331987    9.97424765  32.54048463  -7.19319179]\n",
            "Accuracy -> -1410.0786275731507 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 220.477 ms\n",
            "Fittest individual-> SCORE 3983.2396636782423 | INDIVIDUAL[ 39.77245121 -91.93696811  19.32363107 -66.08170531  12.34110874\n",
            "  67.38602257 -65.81275966 -41.83408562  82.92719188 -26.40663949]\n",
            "Accuracy -> -885.164369706276 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 266.00600000000003 ms\n",
            "Fittest individual-> SCORE 3256.491081695674 | INDIVIDUAL[ 25.7974075  -66.30074687 -39.03496111 -23.6028016   24.34643865\n",
            "  50.56952429 -61.60878318 -35.76175202  77.10517867 -54.73229113]\n",
            "Accuracy -> -723.664684821261 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 218.621 ms\n",
            "Fittest individual-> SCORE 3512.7948004956647 | INDIVIDUAL[ -4.79450403 -13.67614293 -34.40891493 -66.76633045  44.42431349\n",
            "  48.90319154 -84.2154732  -16.480634    62.23989762 -32.14726708]\n",
            "Accuracy -> -780.6210667768144 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 220.62599999999998 ms\n",
            "Fittest individual-> SCORE 3229.114788429399 | INDIVIDUAL[  8.27795455 -73.48268993   7.36705354 -47.25405561  57.04888325\n",
            "  26.30388346 -90.55543345 -29.5669025   99.95377958 -36.78264683]\n",
            "Accuracy -> -717.5810640954221 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 221.774 ms\n",
            "Fittest individual-> SCORE 1759.79471492308 | INDIVIDUAL[ 10.49097912 -84.17567968  15.60856326 -75.76748436  83.17117355\n",
            "  21.32042226 -71.89365063 -35.32639613  76.9793656  -26.12515691]\n",
            "Accuracy -> -391.06549220512886 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 230.58200000000002 ms\n",
            "Fittest individual-> SCORE 11426.897440582681 | INDIVIDUAL[-44.43592761   9.99105222 -44.49420014 -26.93679563  24.63002022\n",
            "  68.72941272 -57.4166459  -30.22905659  34.71504747 -42.32794607]\n",
            "Accuracy -> -2539.310542351707 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 227.535 ms\n",
            "Fittest individual-> SCORE 4966.022607315498 | INDIVIDUAL[ 22.20824384 -57.68441462 -67.20002915 -17.11204712  22.58960723\n",
            "  87.84703104 -77.3357701  -55.14216407  66.71331543 -27.33048352]\n",
            "Accuracy -> -1103.560579403444 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 230.75300000000001 ms\n",
            "Fittest individual-> SCORE 2217.3079106870196 | INDIVIDUAL[ -2.54985869 -44.26081049  -8.65578913 -76.89712573  61.42718763\n",
            "  33.47716671 -65.54172508 -25.84557826  85.02564834 -60.09652338]\n",
            "Accuracy -> -492.7350912637821 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 236.068 ms\n",
            "Fittest individual-> SCORE 606.2259940587257 | INDIVIDUAL[ 48.12996837 -75.14545405 -20.2096392  -96.6244468   76.78575035\n",
            "  45.44599043 -80.78601492 -21.80616234  69.86465189 -48.92642843]\n",
            "Accuracy -> -134.71688756860573 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 231.197 ms\n",
            "Fittest individual-> SCORE 1205.1607364135102 | INDIVIDUAL[ 33.9082275  -82.14699694 -28.09235799 -38.81374651  52.07549265\n",
            "  49.61060548 -90.6384262  -12.00169826  47.62636344 -16.21768315]\n",
            "Accuracy -> -267.81349698078003 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 249.302 ms\n",
            "Fittest individual-> SCORE 1341.2724559894461 | INDIVIDUAL[ 15.46657519 -44.93735062 -18.47531094 -85.76353254  50.63656837\n",
            "  63.55159478 -75.97037852 -28.32752141  70.81054722 -32.6386212 ]\n",
            "Accuracy -> -298.0605457754325 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 218.038 ms\n",
            "Fittest individual-> SCORE 2525.2380804468567 | INDIVIDUAL[  5.88993164 -76.34159419  32.207705   -96.79310711  78.54604856\n",
            "  19.36118858 -59.70959216 -37.64873252  83.64354356 -53.7327939 ]\n",
            "Accuracy -> -561.1640178770792 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 217.172 ms\n",
            "Fittest individual-> SCORE 912.3463275120544 | INDIVIDUAL[ 33.12078108 -84.5971354  -12.90673258 -55.25365384  48.58428689\n",
            "  48.61375521 -75.59636662 -30.30115964  57.95703753 -29.66285261]\n",
            "Accuracy -> -202.74362833601208 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "t_JnkHv85DJt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Running Experiment F8\n",
        "## Hyperparameters\n",
        "VECTOR_LENGTH = 10\n",
        "GENERATION_ITER = 250\n",
        "POPULATION_LENGTH = 6\n",
        "BOUNDS = [-32,32]\n",
        "ELITE_RATE = 0.5\n",
        "OBJECTIVE_FUNC = f8\n",
        "exp2_f8 = run_experiment(OBJECTIVE_FUNC,VECTOR_LENGTH,GENERATION_ITER,POPULATION_LENGTH,BOUNDS,ELITE_RATE,experiment_runs)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ojj_T5x5EA0",
        "outputId": "8b948fcd-4a0a-4aba-9621-1a305a275bb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUN  1\n",
            "Time taken-> 205.231 ms\n",
            "Fittest individual-> SCORE -119.22005508746334 | INDIVIDUAL[-18.80006786 -10.37383301  11.26126943 -13.26438095  -8.86382691\n",
            "  28.99414501  -4.70937793   6.86611268  27.74225514  19.21561753]\n",
            "Accuracy -> 85.15718220533095 %\n",
            "#####################################\n",
            "RUN  2\n",
            "Time taken-> 223.748 ms\n",
            "Fittest individual-> SCORE -119.2927092015405 | INDIVIDUAL[-24.51511502  29.71014612 -26.39079296 -17.54698255 -28.32855016\n",
            "  -0.13290934   4.48639305   0.346715    28.14766484 -11.99107531]\n",
            "Accuracy -> 85.20907800110035 %\n",
            "#####################################\n",
            "RUN  3\n",
            "Time taken-> 198.063 ms\n",
            "Fittest individual-> SCORE -119.18851802927355 | INDIVIDUAL[ 18.61853637 -24.16944752   4.06752859  15.41957886 -18.75292352\n",
            "  11.1708501   25.54422372 -11.1693541  -12.80237918  15.50368398]\n",
            "Accuracy -> 85.1346557351954 %\n",
            "#####################################\n",
            "RUN  4\n",
            "Time taken-> 229.47400000000002 ms\n",
            "Fittest individual-> SCORE -119.19712326168923 | INDIVIDUAL[  1.66363306  22.74736654  -0.41110247  23.53563511 -25.06596333\n",
            "  -6.2753739   16.97587291  -9.01908191  26.17276765  28.39057545]\n",
            "Accuracy -> 85.14080232977803 %\n",
            "#####################################\n",
            "RUN  5\n",
            "Time taken-> 195.417 ms\n",
            "Fittest individual-> SCORE -119.1612037780238 | INDIVIDUAL[-26.70540481  18.75472416  25.17595233  26.99744949  -7.41397235\n",
            "  27.47110927   6.45880745 -19.474248    11.55843536  -3.54914315]\n",
            "Accuracy -> 85.11514555573129 %\n",
            "#####################################\n",
            "RUN  6\n",
            "Time taken-> 195.88 ms\n",
            "Fittest individual-> SCORE -119.300790125514 | INDIVIDUAL[-25.30847259 -14.8595256   19.14790736  22.92917636  -4.95740371\n",
            "  15.74330381 -27.1678577    8.33056711 -25.69538505   9.47710598]\n",
            "Accuracy -> 85.21485008965286 %\n",
            "#####################################\n",
            "RUN  7\n",
            "Time taken-> 246.424 ms\n",
            "Fittest individual-> SCORE -119.16244506370353 | INDIVIDUAL[ 26.62574953 -31.57506357  11.23659521   0.84453155 -15.35214942\n",
            "  29.08556698  27.50366281 -25.85062798  -2.55460594 -17.90444755]\n",
            "Accuracy -> 85.11603218835967 %\n",
            "#####################################\n",
            "RUN  8\n",
            "Time taken-> 198.595 ms\n",
            "Fittest individual-> SCORE -119.01803895411827 | INDIVIDUAL[-22.09952267  16.69270798   1.86603972 -19.82150182  27.94972518\n",
            " -18.30170791  17.66241385  -9.33938798  -3.80673621  -6.79972013]\n",
            "Accuracy -> 85.01288496722734 %\n",
            "#####################################\n",
            "RUN  9\n",
            "Time taken-> 193.215 ms\n",
            "Fittest individual-> SCORE -119.27742211930489 | INDIVIDUAL[-31.41863169   8.40370672 -30.60122873 -16.74922542  10.86811912\n",
            "   0.34831722   0.8472418  -24.60093696  23.88164082 -28.54048896]\n",
            "Accuracy -> 85.19815865664636 %\n",
            "#####################################\n",
            "RUN  10\n",
            "Time taken-> 204.604 ms\n",
            "Fittest individual-> SCORE -119.4990054832058 | INDIVIDUAL[ 17.93534716  -7.58352293  21.86746458  11.67281866  -2.24924872\n",
            " -21.71319791 -14.42006804 -19.25874159 -16.10048316   9.63074365]\n",
            "Accuracy -> 85.35643248800415 %\n",
            "#####################################\n",
            "RUN  11\n",
            "Time taken-> 232.49800000000002 ms\n",
            "Fittest individual-> SCORE -119.07069128478267 | INDIVIDUAL[  7.75785542  -6.58351826  -9.30963266  14.88185501   9.40396753\n",
            " -25.41527517  27.64370474  30.01948438  21.94080056  -2.79377634]\n",
            "Accuracy -> 85.05049377484475 %\n",
            "#####################################\n",
            "RUN  12\n",
            "Time taken-> 197.238 ms\n",
            "Fittest individual-> SCORE -119.19385951803193 | INDIVIDUAL[ 0.1509478   7.42318203 23.99577391 28.42176738 22.50149118  5.76738154\n",
            " -3.78035794 20.91954956 -7.14564087 26.27642647]\n",
            "Accuracy -> 85.13847108430852 %\n",
            "#####################################\n",
            "RUN  13\n",
            "Time taken-> 198.192 ms\n",
            "Fittest individual-> SCORE -119.40496324507885 | INDIVIDUAL[ 13.9504499   29.11416996  19.98298674 -11.46866069  -9.67010159\n",
            " -21.03317816   3.26940393  24.87975177 -25.13561037 -26.70246274]\n",
            "Accuracy -> 85.28925946077061 %\n",
            "#####################################\n",
            "RUN  14\n",
            "Time taken-> 188.35299999999998 ms\n",
            "Fittest individual-> SCORE -119.15885892142772 | INDIVIDUAL[-14.60966029 -29.70911249  -6.73358568 -28.29536718 -25.44868367\n",
            " -24.1046691  -11.11874594  24.26566646   8.83270748  15.08375735]\n",
            "Accuracy -> 85.11347065816265 %\n",
            "#####################################\n",
            "RUN  15\n",
            "Time taken-> 191.18699999999998 ms\n",
            "Fittest individual-> SCORE -119.23106014363057 | INDIVIDUAL[  0.74917368  25.22441104   8.25787913  -4.83697723  13.61534771\n",
            "   3.67099608  14.57342535 -30.90968154 -19.55288468   2.42533657]\n",
            "Accuracy -> 85.16504295973613 %\n",
            "#####################################\n",
            "RUN  16\n",
            "Time taken-> 233.037 ms\n",
            "Fittest individual-> SCORE -119.41168921266478 | INDIVIDUAL[15.99889972 14.88777026 14.0315357  17.79458536  2.80877351  0.55595241\n",
            " 19.29603298 27.85803079 23.3296215  26.75832661]\n",
            "Accuracy -> 85.29406372333199 %\n",
            "#####################################\n",
            "RUN  17\n",
            "Time taken-> 193.95499999999998 ms\n",
            "Fittest individual-> SCORE -119.32683509020372 | INDIVIDUAL[-16.63060644  21.14173082 -20.91813118 -17.24501007   2.92639502\n",
            "   3.91766417  22.36141746  -7.27804439  24.79872755 -22.23591151]\n",
            "Accuracy -> 85.2334536358598 %\n",
            "#####################################\n",
            "RUN  18\n",
            "Time taken-> 194.897 ms\n",
            "Fittest individual-> SCORE -119.0869001082272 | INDIVIDUAL[ 20.56065692 -11.94993943 -10.9311159   22.73771202   2.06968103\n",
            " -28.20694962 -22.83871193 -25.59514061  15.01433829 -22.98782087]\n",
            "Accuracy -> 85.06207150587657 %\n",
            "#####################################\n",
            "RUN  19\n",
            "Time taken-> 232.808 ms\n",
            "Fittest individual-> SCORE -119.46327430784027 | INDIVIDUAL[ 25.42299667  16.68387212  -2.29942251  -2.48108481  -9.28050937\n",
            " -30.27939792 -31.07313976 -28.32033778  -0.78656054 -10.01561775]\n",
            "Accuracy -> 85.33091021988591 %\n",
            "#####################################\n",
            "RUN  20\n",
            "Time taken-> 192.532 ms\n",
            "Fittest individual-> SCORE -119.22071889808765 | INDIVIDUAL[-29.39042939  20.69733666 -24.91595232  21.07353925 -21.8113271\n",
            "   5.15637238  10.04258651  12.9179296  -21.62236421 -20.2121238 ]\n",
            "Accuracy -> 85.15765635577691 %\n",
            "#####################################\n",
            "RUN  21\n",
            "Time taken-> 226.332 ms\n",
            "Fittest individual-> SCORE -119.23796064702879 | INDIVIDUAL[ -4.19235177 -31.65060164  -6.47256524  29.64039441 -11.51307687\n",
            " -15.32225129  24.14206445  22.09570707 -16.40107857 -26.64054882]\n",
            "Accuracy -> 85.16997189073484 %\n",
            "#####################################\n",
            "RUN  22\n",
            "Time taken-> 234.86599999999999 ms\n",
            "Fittest individual-> SCORE -119.19143928420564 | INDIVIDUAL[ -5.30584431  29.77763651 -27.15419508 -30.14003309  10.81981057\n",
            "  -8.15081743  -2.72884006  26.38211978  25.21124454  25.44565064]\n",
            "Accuracy -> 85.13674234586117 %\n",
            "#####################################\n",
            "RUN  23\n",
            "Time taken-> 221.184 ms\n",
            "Fittest individual-> SCORE -119.42844961119988 | INDIVIDUAL[  6.38563361   5.55359004 -22.87387434 -20.10754319   4.83288338\n",
            " -12.67044023 -25.16147096  10.45787246  12.61135969  -2.49473816]\n",
            "Accuracy -> 85.30603543657135 %\n",
            "#####################################\n",
            "RUN  24\n",
            "Time taken-> 197.741 ms\n",
            "Fittest individual-> SCORE -119.2815003672812 | INDIVIDUAL[ -4.43973371   2.0672554   20.25452015  -2.35820324  18.31625786\n",
            "   9.37911214   1.59875524 -27.99271715 -23.98555726   2.58989262]\n",
            "Accuracy -> 85.20107169091514 %\n",
            "#####################################\n",
            "RUN  25\n",
            "Time taken-> 233.646 ms\n",
            "Fittest individual-> SCORE -119.05740529241092 | INDIVIDUAL[-16.38236751  26.77138973  -4.22468366   2.48392291   9.86602213\n",
            "  -4.94535747  16.20777338  11.83212516  26.76942202  20.98175675]\n",
            "Accuracy -> 85.04100378029351 %\n",
            "#####################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis"
      ],
      "metadata": {
        "id": "y6uEWC4s5Q0A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1, F4 ,F8"
      ],
      "metadata": {
        "id": "p2ChBmqU5Q0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_axis = [i for i in range(experiment_runs)]\n",
        "plt.plot(x_axis, exp2_f1['accuracies'])\n",
        "plt.plot(x_axis, exp2_f4['accuracies'])\n",
        "plt.plot(x_axis, exp2_f8['accuracies'])\n",
        "\n",
        "plt.title('Accuracy vs Runs')\n",
        "plt.xlabel('Runs')\n",
        "plt.ylabel('Accuracy(%)')\n",
        "plt.legend(['F1', 'F4','F8'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f1975c7-8d89-4108-f4aa-7a99dbd17728",
        "id": "_a4pOFAl5Q0B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F1"
      ],
      "metadata": {
        "id": "vlkuVrJi5Q0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F1 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp2_f1['runs'][0]))]\n",
        "plt.plot(x_axis, exp2_f1['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "956e7210-a5e3-4990-bfbe-07a563929c31",
        "id": "wfZOYYTZ5Q0B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F4"
      ],
      "metadata": {
        "id": "eYFxP3o75Q0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F4 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp2_f4['runs'][0]))]\n",
        "plt.plot(x_axis, exp2_f4['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3d94d96-df6c-4f02-df5a-97ed0af89b50",
        "id": "Pf5D_kAT5Q0B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### F8"
      ],
      "metadata": {
        "id": "6V-Ap6BA5Q0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## F8 Convergence Single Run\n",
        "x_axis = [i for i in range(len(exp2_f8['runs'][0]))]\n",
        "plt.plot(x_axis, exp2_f8['runs'][0])\n",
        "plt.title('Convergence')\n",
        "plt.xlabel('Number of iterations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03b7e302-8034-4a86-f66d-a8f239299589",
        "id": "Mpfz08zf5Q0B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_exp2_f1 = np.max(exp2_f1['accuracies'])\n",
        "print('Best F1 Accuracy->',best_exp2_f1)\n",
        "best_exp2_f4 = np.max(exp2_f4['accuracies'])\n",
        "print('Best F4 Accuracy->',best_exp2_f4)\n",
        "best_exp2_f8 = np.max(exp2_f8['accuracies'])\n",
        "print('Best F8 Accuracy->',best_exp2_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f62b6df-22e6-4cc7-dc46-eee8b443e524",
        "id": "bZHMyhq35Q0B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 Accuracy-> 95.53361797615088\n",
            "Best F4 Accuracy-> -78.69749198594135\n",
            "Best F8 Accuracy-> 85.35643248800415\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "worst_exp2_f1 = np.min(exp2_f1['accuracies'])\n",
        "print('Worst F1 Accuracy->',worst_exp2_f1)\n",
        "worst_exp2_f4 = np.min(exp2_f4['accuracies'])\n",
        "print('Worst F4 Accuracy->',worst_exp2_f4)\n",
        "worst_exp2_f8 = np.min(exp2_f8['accuracies'])\n",
        "print('Worst F8 Accuracy->',worst_exp2_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b148671-c3eb-4b5c-fb4d-0da048673929",
        "id": "KXTJN2CY5Q0C"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Worst F1 Accuracy-> 73.2390109135311\n",
            "Worst F4 Accuracy-> -2539.310542351707\n",
            "Worst F8 Accuracy-> 85.01288496722734\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "median_exp2_f1 = np.median(exp2_f1['accuracies'])\n",
        "print('median F1 Accuracy->',median_exp2_f1)\n",
        "median_exp2_f4 = np.median(exp2_f4['accuracies'])\n",
        "print('median F4 Accuracy->',median_exp2_f4)\n",
        "median_exp2_f8 = np.median(exp2_f8['accuracies'])\n",
        "print('median F8 Accuracy->',median_exp2_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bffcb0a-d2ef-447b-d3b3-ea2654a27fcb",
        "id": "WrFAkJKV5Q0C"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "median F1 Accuracy-> 90.1074589378354\n",
            "median F4 Accuracy-> -391.06549220512886\n",
            "median F8 Accuracy-> 85.15765635577691\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp2_f1 = mean(exp2_f1['accuracies'])\n",
        "accuracy_exp2_f1 = mean(exp2_f1['accuracies'][0:20])\n",
        "efficiency_exp2_f1 = mean(exp2_f1['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp2_f1)\n",
        "print(\"Accuracy\",accuracy_exp2_f1)\n",
        "print(\"Efficiency\",efficiency_exp2_f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fdm6jksZ6cmf",
        "outputId": "94e2f3cb-bbad-4435-bec1-ea45789305ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 88.69368060657602\n",
            "Accuracy 88.48042900345388\n",
            "Efficiency 211.812\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp2_f4 = mean(exp2_f4['accuracies'])\n",
        "accuracy_exp2_f4 = mean(exp2_f4['accuracies'][0:20])\n",
        "efficiency_exp2_f4 = mean(exp2_f4['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp2_f4)\n",
        "print(\"Accuracy\",accuracy_exp2_f4)\n",
        "print(\"Efficiency\",efficiency_exp2_f4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqtY9p74649J",
        "outputId": "b8d9e0e8-ab20-4ee3-a423-9011d729d787"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability -592.6949980519876\n",
            "Accuracy -667.6438187380891\n",
            "Efficiency 230.12292\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reliability_exp2_f8 = mean(exp2_f8['accuracies'])\n",
        "accuracy_exp2_f8 = mean(exp2_f8['accuracies'][0:20])\n",
        "efficiency_exp2_f8 = mean(exp2_f8['timedRuns'])\n",
        "\n",
        "print(\"Reliability\",reliability_exp2_f8)\n",
        "print(\"Accuracy\",accuracy_exp2_f8)\n",
        "print(\"Efficiency\",efficiency_exp2_f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCYnt7SV7Dvh",
        "outputId": "064b6187-8d80-45f3-c64a-fea0e1a31f95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reliability 85.17379762959825\n",
            "Accuracy 85.17450577977901\n",
            "Efficiency 210.36468\n"
          ]
        }
      ]
    }
  ]
}