src/genetic_algorithm_battleship.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import battleship as ship\n",
    "import genetic_algorithm as ga\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting Up the Board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of pieces on board\n",
      "0000111000000000000000000111100000000000000001000000010100000001010000000101000010000100001000000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Battleship Board')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a new board and solution\n",
    "board, solution = ship.new_board()\n",
    "\n",
    "# print the genetic solution\n",
    "print(solution)\n",
    "\n",
    "# add the board to a dataframe for plotting\n",
    "df = pd.DataFrame(board, columns=['A','B','C','D','E','F','G','H','I','J',])\n",
    "df['ylabel'] = range(10,0,-1)\n",
    "df = df.set_index('ylabel')\n",
    "\n",
    "# print the board using a heatmap\n",
    "fig, ax = plt.subplots(1, 1, figsize = (10, 8))\n",
    "sns.heatmap(df, cmap='coolwarm', linewidths=0.5, annot=True, cbar=False)\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('')\n",
    "ax.set_title('Battleship Board')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Random Guesses of the Entire Board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Battleship Board')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a random guess\n",
    "rand_sol = np.random.randint(2, size=100).reshape((10, 10))\n",
    "print(sum(sum(rand_sol)))\n",
    "\n",
    "# overlay a random guess on the board using a heatmap\n",
    "rand_df = pd.DataFrame(rand_sol, columns=['A','B','C','D','E','F','G','H','I','J',])\n",
    "rand_df['ylabel'] = range(10,0,-1)\n",
    "rand_df = rand_df.set_index('ylabel')\n",
    "\n",
    "# print the random guess on the board using a heatmap\n",
    "fig, ay = plt.subplots(1, 1, figsize = (10, 8))\n",
    "sns.heatmap(rand_df, cmap='coolwarm', linewidths=0.5, annot=board, cbar=False)\n",
    "ay.set_ylabel('')    \n",
    "ay.set_xlabel('')\n",
    "ay.set_title('Battleship Board')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.52\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Battleship Board')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show accuracy of the random guess\n",
    "rand_sol_str = ''.join(str(x) for x in list(rand_sol.flatten()))\n",
    "gene_matches = []\n",
    "n_gene_matches = 0\n",
    "    \n",
    "for i in range(len(solution)):\n",
    "    if solution[i] == rand_sol_str[i]:\n",
    "        n_gene_matches += 1\n",
    "        gene_matches.append(0)\n",
    "    else:\n",
    "        gene_matches.append(1)\n",
    "            \n",
    "accuracy = n_gene_matches / len(solution)\n",
    "print('Accuracy: ' + str(accuracy))\n",
    "\n",
    "gene_matches_arr = np.array(gene_matches).reshape((10, 10))\n",
    "\n",
    "# overlay a random guess on the board using a heatmap\n",
    "rand_df = pd.DataFrame(gene_matches_arr, columns=['A','B','C','D','E','F','G','H','I','J',])\n",
    "rand_df['ylabel'] = range(10,0,-1)\n",
    "rand_df = rand_df.set_index('ylabel')\n",
    "\n",
    "# print the random guess on the board using a heatmap\n",
    "fig, az = plt.subplots(1, 1, figsize = (10, 8))\n",
    "rg = sns.diverging_palette(133, 10, as_cmap=True)\n",
    "sns.heatmap(rand_df, cmap=rg, linewidths=0.5, annot=board, cbar=False)\n",
    "az.set_ylabel('')    \n",
    "az.set_xlabel('')\n",
    "az.set_title('Battleship Board')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Descendent Generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run genetic algoritm to solve board\n",
    "generation_size = 10\n",
    "gene_pool = ga.solve(solution, generation_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Chromosome</th>\n",
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       "      <td>0.54</td>\n",
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       "      <td>0111011011010100101110000100001110000101110100...</td>\n",
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       "      <td>0.53</td>\n",
       "      <td>0</td>\n",
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       "      <th>5</th>\n",
       "      <td>0010011111010111101000010110101111101001001100...</td>\n",
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       "      <td>0.48</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0010100101111111111111110010011000010101110010...</td>\n",
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       "      <td>0.44</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Chromosome Generation  \\\n",
       "Sequence                                                                 \n",
       "1         1100110000100101000010110111010000010000101001...          1   \n",
       "8         1001100000100010010011000010110100001111000010...          1   \n",
       "2         1000011100110110101000011111000111001101100001...          1   \n",
       "7         1101101001000100011110010101101101000101011001...          1   \n",
       "9         0111011011010100101110000100001110000101110100...          1   \n",
       "6         0001111110000110100010110111001001101100110111...          1   \n",
       "4         0000000010000111101100011000101100110110111100...          1   \n",
       "3         1000000110011010011101100011100001011111000100...          1   \n",
       "5         0010011111010111101000010110101111101001001100...          1   \n",
       "10        0010100101111111111111110010011000010101110010...          1   \n",
       "\n",
       "           Birth  Fitness Parents  Elite  \n",
       "Sequence                                  \n",
       "1         Random     0.57       0   True  \n",
       "8         Random     0.57       0   True  \n",
       "2         Random     0.55       0  False  \n",
       "7         Random     0.54       0  False  \n",
       "9         Random     0.53       0  False  \n",
       "6         Random     0.52       0  False  \n",
       "4         Random     0.50       0  False  \n",
       "3         Random     0.48       0  False  \n",
       "5         Random     0.48       0  False  \n",
       "10        Random     0.44       0  False  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first generation\n",
    "gene_pool.head(generation_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1100110000100101000010110111010000010000101001...</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1100110000100101000010110111010000010000101001...</td>\n",
       "      <td>2</td>\n",
       "      <td>Mutation</td>\n",
       "      <td>0.57</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1100110000100101000010110111010000010000101001...</td>\n",
       "      <td>2</td>\n",
       "      <td>Mutation</td>\n",
       "      <td>0.57</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1100110000100101000010110111010000010000101001...</td>\n",
       "      <td>2</td>\n",
       "      <td>Mutation</td>\n",
       "      <td>0.57</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1100110000100101000010110111010000010000101001...</td>\n",
       "      <td>2</td>\n",
       "      <td>Mutation</td>\n",
       "      <td>0.57</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1100110000100101000010110111010000010000101001...</td>\n",
       "      <td>2</td>\n",
       "      <td>Mutation</td>\n",
       "      <td>0.57</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1001100000100010010011000010110100001111000010...</td>\n",
       "      <td>2</td>\n",
       "      <td>Splice Pair</td>\n",
       "      <td>0.54</td>\n",
       "      <td>[11, 12]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Chromosome Generation  \\\n",
       "Sequence                                                                 \n",
       "13        1100110000100101000010110111010000010000101001...          2   \n",
       "20        1000110000100101000010110111010000010000101001...          2   \n",
       "11        1100110000100101000010110111010000010000101001...          2   \n",
       "12        1001100000100010010011000010110100001111000010...          2   \n",
       "15        1100110000100101000010110111010000010000101001...          2   \n",
       "16        1100110000100101000010110111010000010000101001...          2   \n",
       "17        1100110000100101000010110111010000010000101001...          2   \n",
       "18        1100110000100101000010110111010000010000101001...          2   \n",
       "19        1100110000100101000010110111010000010000101001...          2   \n",
       "14        1001100000100010010011000010110100001111000010...          2   \n",
       "\n",
       "                Birth  Fitness   Parents  Elite  \n",
       "Sequence                                         \n",
       "13        Splice Pair     0.60  [11, 12]   True  \n",
       "20           Mutation     0.58        11   True  \n",
       "11            Elitism     0.57         1  False  \n",
       "12            Elitism     0.57         8  False  \n",
       "15           Mutation     0.57        11  False  \n",
       "16           Mutation     0.57        11  False  \n",
       "17           Mutation     0.57        11  False  \n",
       "18           Mutation     0.57        11  False  \n",
       "19           Mutation     0.57        11  False  \n",
       "14        Splice Pair     0.54  [11, 12]  False  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the second generation\n",
    "gene_pool.iloc[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
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       "      <td>False</td>\n",
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       "      <th>1544</th>\n",
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       "      <td>[1542, 1541]</td>\n",
       "      <td>False</td>\n",
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       "      <th>1549</th>\n",
       "      <td>0000111000000000000000000111100000000000000001...</td>\n",
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       "      <td>Mutation</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1541</td>\n",
       "      <td>False</td>\n",
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       "      <th>1546</th>\n",
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       "      <td>Mutation</td>\n",
       "      <td>0.98</td>\n",
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       "      <td>Mutation</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1541</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>0000111000000000000000000111100000000000000101...</td>\n",
       "      <td>155</td>\n",
       "      <td>Mutation</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1541</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>0000101000000000000000000111100000000000000001...</td>\n",
       "      <td>155</td>\n",
       "      <td>Mutation</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1542</td>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "                                                 Chromosome Generation  \\\n",
       "Sequence                                                                 \n",
       "1550      0000111000000000000000000111100000000000000001...        155   \n",
       "1541      0000111000000000000000000111100000000000000001...        155   \n",
       "1542      0000111000000000000000000111100000000000000001...        155   \n",
       "1543      0000111000000000000000000111100000000000000001...        155   \n",
       "1544      0000111000000000000000000111100000000000000001...        155   \n",
       "1549      0000111000000000000000000111100000000000000001...        155   \n",
       "1546      0000111000000000000000000111100000000000000001...        155   \n",
       "1545      0000111000000000000000000111100000000000000001...        155   \n",
       "1547      0000111000000000000000000111100000000000000101...        155   \n",
       "1548      0000101000000000000000000111100000000000000001...        155   \n",
       "\n",
       "                Birth  Fitness       Parents  Elite  \n",
       "Sequence                                             \n",
       "1550         Mutation     1.00          1542   True  \n",
       "1541          Elitism     0.99          1531   True  \n",
       "1542          Elitism     0.99          1532  False  \n",
       "1543      Splice Pair     0.99  [1542, 1541]  False  \n",
       "1544      Splice Pair     0.99  [1542, 1541]  False  \n",
       "1549         Mutation     0.99          1541  False  \n",
       "1546         Mutation     0.98          1541  False  \n",
       "1545         Mutation     0.97          1541  False  \n",
       "1547         Mutation     0.97          1541  False  \n",
       "1548         Mutation     0.97          1542  False  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the last generation\n",
    "gene_pool.tail(generation_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Fitness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.042635</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.4850</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.5475</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.014491</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.5700</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.5700</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.014181</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.5825</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.6075</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.005676</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.6100</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.6100</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.006325</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.6100</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.6175</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.004216</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.6200</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.6200</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.008756</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.6225</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.6400</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.6325</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.6400</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.008756</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.6300</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.6475</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.009661</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fitness                                                    \n",
       "             count   mean       std   min     25%    50%     75%   max\n",
       "Generation                                                            \n",
       "1             10.0  0.518  0.042635  0.44  0.4850  0.525  0.5475  0.57\n",
       "2             10.0  0.571  0.014491  0.54  0.5700  0.570  0.5700  0.60\n",
       "3             10.0  0.593  0.014181  0.57  0.5825  0.590  0.6075  0.61\n",
       "4             10.0  0.609  0.005676  0.60  0.6100  0.610  0.6100  0.62\n",
       "5             10.0  0.612  0.006325  0.60  0.6100  0.610  0.6175  0.62\n",
       "6             10.0  0.622  0.004216  0.62  0.6200  0.620  0.6200  0.63\n",
       "7             10.0  0.631  0.008756  0.62  0.6225  0.630  0.6400  0.64\n",
       "8             10.0  0.637  0.004830  0.63  0.6325  0.640  0.6400  0.64\n",
       "9             10.0  0.639  0.008756  0.63  0.6300  0.640  0.6475  0.65\n",
       "10            10.0  0.646  0.009661  0.62  0.6500  0.650  0.6500  0.65"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the fitness stats of the first 10 generations\n",
    "gene_pool_stats = gene_pool.groupby('Generation').describe()\n",
    "gene_pool_stats.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converging to the Genetic Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Fitness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.009487</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.007071</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.014907</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.9725</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.009661</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.9900</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.006325</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.9900</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.010328</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.010593</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.9800</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.006992</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.9825</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.008498</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.9825</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.010750</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.9725</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fitness                                                  \n",
       "             count   mean       std   min     25%    50%   75%   max\n",
       "Generation                                                          \n",
       "146           10.0  0.983  0.009487  0.96  0.9800  0.985  0.99  0.99\n",
       "147           10.0  0.985  0.007071  0.97  0.9800  0.990  0.99  0.99\n",
       "148           10.0  0.980  0.014907  0.95  0.9725  0.990  0.99  0.99\n",
       "149           10.0  0.986  0.009661  0.96  0.9900  0.990  0.99  0.99\n",
       "150           10.0  0.988  0.006325  0.97  0.9900  0.990  0.99  0.99\n",
       "151           10.0  0.982  0.010328  0.96  0.9800  0.985  0.99  0.99\n",
       "152           10.0  0.983  0.010593  0.96  0.9800  0.990  0.99  0.99\n",
       "153           10.0  0.986  0.006992  0.97  0.9825  0.990  0.99  0.99\n",
       "154           10.0  0.985  0.008498  0.97  0.9825  0.990  0.99  0.99\n",
       "155           10.0  0.984  0.010750  0.97  0.9725  0.990  0.99  1.00"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the fitness stats of the last 10 generations\n",
    "gene_pool_stats.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gene Pool Statistics')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the convergence of the gene pool\n",
    "fig, ay = plt.subplots(1, 1, figsize = (10, 8))\n",
    "ay = sns.lineplot(x=\"Generation\", y=\"Fitness\", data=gene_pool)\n",
    "ay.set_title('Gene Pool Statistics')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning the Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Correct number of pieces on board\n",
      "1\n",
      "Correct number of pieces on board\n",
      "2\n",
      "Correct number of pieces on board\n",
      "3\n",
      "Correct number of pieces on board\n",
      "4\n",
      "Correct number of pieces on board\n",
      "5\n",
      "Correct number of pieces on board\n",
      "6\n",
      "Correct number of pieces on board\n",
      "7\n",
      "Correct number of pieces on board\n",
      "8\n",
      "Correct number of pieces on board\n",
      "9\n",
      "Correct number of pieces on board\n",
      "10\n",
      "Correct number of pieces on board\n",
      "11\n",
      "Correct number of pieces on board\n",
      "12\n",
      "Correct number of pieces on board\n",
      "13\n",
      "Correct number of pieces on board\n",
      "14\n",
      "Correct number of pieces on board\n",
      "15\n",
      "Correct number of pieces on board\n",
      "16\n",
      "Correct number of pieces on board\n",
      "17\n",
      "Correct number of pieces on board\n",
      "18\n",
      "Correct number of pieces on board\n",
      "19\n",
      "Correct number of pieces on board\n",
      "20\n",
      "Correct number of pieces on board\n",
      "21\n",
      "Correct number of pieces on board\n",
      "22\n",
      "Correct number of pieces on board\n",
      "23\n",
      "Correct number of pieces on board\n",
      "24\n",
      "Correct number of pieces on board\n",
      "25\n",
      "Correct number of pieces on board\n",
      "26\n",
      "Correct number of pieces on board\n",
      "27\n",
      "Correct number of pieces on board\n",
      "28\n",
      "Correct number of pieces on board\n",
      "29\n",
      "Correct number of pieces on board\n",
      "30\n",
      "Correct number of pieces on board\n",
      "31\n",
      "Correct number of pieces on board\n",
      "32\n",
      "Correct number of pieces on board\n",
      "33\n",
      "Correct number of pieces on board\n",
      "34\n",
      "Correct number of pieces on board\n",
      "35\n",
      "Correct number of pieces on board\n",
      "36\n",
      "Correct number of pieces on board\n",
      "37\n",
      "Correct number of pieces on board\n",
      "38\n",
      "Correct number of pieces on board\n",
      "39\n",
      "Correct number of pieces on board\n",
      "40\n",
      "Correct number of pieces on board\n",
      "41\n",
      "Correct number of pieces on board\n",
      "42\n",
      "Correct number of pieces on board\n",
      "43\n",
      "Correct number of pieces on board\n",
      "44\n",
      "Correct number of pieces on board\n",
      "45\n",
      "Correct number of pieces on board\n",
      "46\n",
      "Correct number of pieces on board\n",
      "47\n",
      "Correct number of pieces on board\n",
      "48\n",
      "Correct number of pieces on board\n",
      "49\n",
      "Correct number of pieces on board\n",
      "50\n",
      "Correct number of pieces on board\n",
      "51\n",
      "Correct number of pieces on board\n",
      "52\n",
      "Correct number of pieces on board\n",
      "53\n",
      "Correct number of pieces on board\n",
      "54\n",
      "Correct number of pieces on board\n",
      "55\n",
      "Correct number of pieces on board\n",
      "56\n",
      "Correct number of pieces on board\n",
      "57\n",
      "Correct number of pieces on board\n",
      "58\n",
      "Correct number of pieces on board\n",
      "59\n",
      "Correct number of pieces on board\n",
      "60\n",
      "Correct number of pieces on board\n",
      "61\n",
      "Correct number of pieces on board\n",
      "62\n",
      "Correct number of pieces on board\n",
      "63\n",
      "Correct number of pieces on board\n",
      "64\n",
      "Correct number of pieces on board\n",
      "65\n",
      "Correct number of pieces on board\n",
      "66\n",
      "Correct number of pieces on board\n",
      "67\n",
      "Correct number of pieces on board\n",
      "68\n",
      "Correct number of pieces on board\n",
      "69\n",
      "Correct number of pieces on board\n",
      "70\n",
      "Correct number of pieces on board\n",
      "71\n",
      "Correct number of pieces on board\n",
      "72\n",
      "Correct number of pieces on board\n",
      "73\n",
      "Correct number of pieces on board\n",
      "74\n",
      "Correct number of pieces on board\n",
      "75\n",
      "Correct number of pieces on board\n",
      "76\n",
      "Correct number of pieces on board\n",
      "77\n",
      "Correct number of pieces on board\n",
      "78\n",
      "Correct number of pieces on board\n",
      "79\n",
      "Correct number of pieces on board\n",
      "80\n",
      "Correct number of pieces on board\n",
      "81\n",
      "Correct number of pieces on board\n",
      "82\n",
      "Correct number of pieces on board\n",
      "83\n",
      "Correct number of pieces on board\n",
      "84\n",
      "Correct number of pieces on board\n",
      "85\n",
      "Correct number of pieces on board\n",
      "86\n",
      "Correct number of pieces on board\n",
      "87\n",
      "Correct number of pieces on board\n",
      "88\n",
      "Correct number of pieces on board\n",
      "89\n",
      "Correct number of pieces on board\n",
      "90\n",
      "Correct number of pieces on board\n",
      "91\n",
      "Correct number of pieces on board\n",
      "92\n",
      "Correct number of pieces on board\n",
      "93\n",
      "Correct number of pieces on board\n",
      "94\n",
      "Correct number of pieces on board\n",
      "95\n",
      "Correct number of pieces on board\n",
      "96\n",
      "Correct number of pieces on board\n",
      "97\n",
      "Correct number of pieces on board\n",
      "98\n",
      "Correct number of pieces on board\n",
      "99\n",
      "Correct number of pieces on board\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "484\n",
      "Correct number of pieces on board\n",
      "485\n",
      "Correct number of pieces on board\n",
      "486\n",
      "Correct number of pieces on board\n",
      "487\n",
      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n",
      "489\n",
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      "505\n",
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      "508\n",
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      "509\n",
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      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n",
      "514\n",
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      "Correct number of pieces on board\n",
      "516\n",
      "Correct number of pieces on board\n",
      "517\n",
      "Correct number of pieces on board\n",
      "518\n",
      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n",
      "520\n",
      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n",
      "522\n",
      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n",
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      "525\n",
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      "526\n",
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      "540\n",
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      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "651\n",
      "Correct number of pieces on board\n",
      "652\n",
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      "Correct number of pieces on board\n",
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      "Correct number of pieces on board\n",
      "866\n",
      "Correct number of pieces on board\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "867\n",
      "Correct number of pieces on board\n",
      "868\n",
      "Correct number of pieces on board\n",
      "869\n",
      "Correct number of pieces on board\n",
      "870\n",
      "Correct number of pieces on board\n",
      "871\n",
      "Correct number of pieces on board\n",
      "872\n",
      "Correct number of pieces on board\n",
      "873\n",
      "Correct number of pieces on board\n",
      "874\n",
      "Correct number of pieces on board\n",
      "875\n",
      "Correct number of pieces on board\n",
      "876\n",
      "Correct number of pieces on board\n",
      "877\n",
      "Correct number of pieces on board\n",
      "878\n",
      "Correct number of pieces on board\n",
      "879\n",
      "Correct number of pieces on board\n",
      "880\n",
      "Correct number of pieces on board\n",
      "881\n",
      "Correct number of pieces on board\n",
      "882\n",
      "Correct number of pieces on board\n",
      "883\n",
      "Correct number of pieces on board\n",
      "884\n",
      "Correct number of pieces on board\n",
      "885\n",
      "Correct number of pieces on board\n",
      "886\n",
      "Correct number of pieces on board\n",
      "887\n",
      "Correct number of pieces on board\n",
      "888\n",
      "Correct number of pieces on board\n",
      "889\n",
      "Correct number of pieces on board\n",
      "890\n",
      "Correct number of pieces on board\n",
      "891\n",
      "Correct number of pieces on board\n",
      "892\n",
      "Correct number of pieces on board\n",
      "893\n",
      "Correct number of pieces on board\n",
      "894\n",
      "Correct number of pieces on board\n",
      "895\n",
      "Correct number of pieces on board\n",
      "896\n",
      "Correct number of pieces on board\n",
      "897\n",
      "Correct number of pieces on board\n",
      "898\n",
      "Correct number of pieces on board\n",
      "899\n",
      "Correct number of pieces on board\n",
      "900\n",
      "Correct number of pieces on board\n",
      "901\n",
      "Correct number of pieces on board\n",
      "902\n",
      "Correct number of pieces on board\n",
      "903\n",
      "Correct number of pieces on board\n",
      "904\n",
      "Correct number of pieces on board\n",
      "905\n",
      "Correct number of pieces on board\n",
      "906\n",
      "Correct number of pieces on board\n",
      "907\n",
      "Correct number of pieces on board\n",
      "908\n",
      "Correct number of pieces on board\n",
      "909\n",
      "Correct number of pieces on board\n",
      "910\n",
      "Correct number of pieces on board\n",
      "911\n",
      "Correct number of pieces on board\n",
      "912\n",
      "Correct number of pieces on board\n",
      "913\n",
      "Correct number of pieces on board\n",
      "914\n",
      "Correct number of pieces on board\n",
      "915\n",
      "Correct number of pieces on board\n",
      "916\n",
      "Correct number of pieces on board\n",
      "917\n",
      "Correct number of pieces on board\n",
      "918\n",
      "Correct number of pieces on board\n",
      "919\n",
      "Correct number of pieces on board\n",
      "920\n",
      "Correct number of pieces on board\n",
      "921\n",
      "Correct number of pieces on board\n",
      "922\n",
      "Correct number of pieces on board\n",
      "923\n",
      "Correct number of pieces on board\n",
      "924\n",
      "Correct number of pieces on board\n",
      "925\n",
      "Correct number of pieces on board\n",
      "926\n",
      "Correct number of pieces on board\n",
      "927\n",
      "Correct number of pieces on board\n",
      "928\n",
      "Correct number of pieces on board\n",
      "929\n",
      "Correct number of pieces on board\n",
      "930\n",
      "Correct number of pieces on board\n",
      "931\n",
      "Correct number of pieces on board\n",
      "932\n",
      "Correct number of pieces on board\n",
      "933\n",
      "Correct number of pieces on board\n",
      "934\n",
      "Correct number of pieces on board\n",
      "935\n",
      "Correct number of pieces on board\n",
      "936\n",
      "Correct number of pieces on board\n",
      "937\n",
      "Correct number of pieces on board\n",
      "938\n",
      "Correct number of pieces on board\n",
      "939\n",
      "Correct number of pieces on board\n",
      "940\n",
      "Correct number of pieces on board\n",
      "941\n",
      "Correct number of pieces on board\n",
      "942\n",
      "Correct number of pieces on board\n",
      "943\n",
      "Correct number of pieces on board\n",
      "944\n",
      "Correct number of pieces on board\n",
      "945\n",
      "Correct number of pieces on board\n",
      "946\n",
      "Correct number of pieces on board\n",
      "947\n",
      "Correct number of pieces on board\n",
      "948\n",
      "Correct number of pieces on board\n",
      "949\n",
      "Correct number of pieces on board\n",
      "950\n",
      "Correct number of pieces on board\n",
      "951\n",
      "Correct number of pieces on board\n",
      "952\n",
      "Correct number of pieces on board\n",
      "953\n",
      "Correct number of pieces on board\n",
      "954\n",
      "Correct number of pieces on board\n",
      "955\n",
      "Correct number of pieces on board\n",
      "956\n",
      "Correct number of pieces on board\n",
      "957\n",
      "Correct number of pieces on board\n",
      "958\n",
      "Correct number of pieces on board\n",
      "959\n",
      "Correct number of pieces on board\n",
      "960\n",
      "Correct number of pieces on board\n",
      "961\n",
      "Correct number of pieces on board\n",
      "962\n",
      "Correct number of pieces on board\n",
      "963\n",
      "Correct number of pieces on board\n",
      "964\n",
      "Correct number of pieces on board\n",
      "965\n",
      "Correct number of pieces on board\n",
      "966\n",
      "Correct number of pieces on board\n",
      "967\n",
      "Correct number of pieces on board\n",
      "968\n",
      "Correct number of pieces on board\n",
      "969\n",
      "Correct number of pieces on board\n",
      "970\n",
      "Correct number of pieces on board\n",
      "971\n",
      "Correct number of pieces on board\n",
      "972\n",
      "Correct number of pieces on board\n",
      "973\n",
      "Correct number of pieces on board\n",
      "974\n",
      "Correct number of pieces on board\n",
      "975\n",
      "Correct number of pieces on board\n",
      "976\n",
      "Correct number of pieces on board\n",
      "977\n",
      "Correct number of pieces on board\n",
      "978\n",
      "Correct number of pieces on board\n",
      "979\n",
      "Correct number of pieces on board\n",
      "980\n",
      "Correct number of pieces on board\n",
      "981\n",
      "Correct number of pieces on board\n",
      "982\n",
      "Correct number of pieces on board\n",
      "983\n",
      "Correct number of pieces on board\n",
      "984\n",
      "Correct number of pieces on board\n",
      "985\n",
      "Correct number of pieces on board\n",
      "986\n",
      "Correct number of pieces on board\n",
      "987\n",
      "Correct number of pieces on board\n",
      "988\n",
      "Correct number of pieces on board\n",
      "989\n",
      "Correct number of pieces on board\n",
      "990\n",
      "Correct number of pieces on board\n",
      "991\n",
      "Correct number of pieces on board\n",
      "992\n",
      "Correct number of pieces on board\n",
      "993\n",
      "Correct number of pieces on board\n",
      "994\n",
      "Correct number of pieces on board\n",
      "995\n",
      "Correct number of pieces on board\n",
      "996\n",
      "Correct number of pieces on board\n",
      "997\n",
      "Correct number of pieces on board\n",
      "998\n",
      "Correct number of pieces on board\n",
      "999\n",
      "Correct number of pieces on board\n"
     ]
    }
   ],
   "source": [
    "# solve 10,000 boards with genetic algorithm\n",
    "gene_pool_samples = []\n",
    "\n",
    "for i in range(1000):\n",
    "    print(i)\n",
    "    \n",
    "    #create a new board and solution\n",
    "    board, solution = ship.new_board()\n",
    "    \n",
    "    # run genetic algoritm to solve board\n",
    "    generation_size = 10\n",
    "    gene_pool = ga.solve(solution, generation_size)\n",
    "    \n",
    "    gene_pool_samples.append(gene_pool.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Performance for 1000 Genetic Algorithm Samples')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a histogram for gene pool samples\n",
    "fig, aa = plt.subplots(1, 1, figsize = (10, 8))\n",
    "aa = sns.distplot(gene_pool_samples)\n",
    "aa.set_xlabel('Total Trial Solutions to Achieve 100% Fitness')    \n",
    "aa.set_ylabel('Distribution Density')\n",
    "aa.set_title('Performance for 1000 Genetic Algorithm Samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}