one_max_problem.ipynb

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   "name": "ga",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from ga_tools.optimization import GenericAlgorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Start of evolution\n",
      "Evaluated 100 individuals\n",
      "-- Generation 1 --\n",
      "{'avg': 3.0754388534362795, 'std': 0.43300833516849857, 'min': 2.46959241239604, 'max': 4.02854841553306}\n",
      "-- Generation 2 --\n",
      "{'avg': 3.398588297497438, 'std': 0.34496274039101044, 'min': 2.94982181062246, 'max': 4.2927019155779025}\n",
      "-- Generation 3 --\n",
      "{'avg': 3.6016667468018078, 'std': 0.30397114937540953, 'min': 3.1597737598729396, 'max': 4.2927019155779025}\n",
      "-- Generation 4 --\n",
      "{'avg': 3.791791233056683, 'std': 0.24868064958191596, 'min': 3.416818532962104, 'max': 4.2927019155779025}\n",
      "-- Generation 5 --\n",
      "{'avg': 3.936874653452477, 'std': 0.2021176713663049, 'min': 3.6146679750290356, 'max': 4.332723844015084}\n",
      "-- Generation 6 --\n",
      "{'avg': 4.031994399959919, 'std': 0.17109226251596907, 'min': 3.8175297541928757, 'max': 4.332723844015084}\n",
      "-- Generation 7 --\n",
      "{'avg': 4.136950134768004, 'std': 0.14645121218416668, 'min': 3.839387748617957, 'max': 4.56160461141991}\n",
      "-- Generation 8 --\n",
      "{'avg': 4.210282663712115, 'std': 0.10459801537165293, 'min': 4.115834166473691, 'max': 4.56160461141991}\n",
      "-- Generation 9 --\n",
      "{'avg': 4.279076895161457, 'std': 0.1355703375018851, 'min': 4.178431598783044, 'max': 4.824384586757542}\n",
      "-- Generation 10 --\n",
      "{'avg': 4.3270135912710055, 'std': 0.1436140220451452, 'min': 4.182835312614993, 'max': 4.824384586757542}\n",
      "-- Generation 11 --\n",
      "{'avg': 4.393494370606832, 'std': 0.13064768390538672, 'min': 4.221937244556067, 'max': 4.824384586757542}\n",
      "-- Generation 12 --\n",
      "{'avg': 4.472806064431896, 'std': 0.12885994480423113, 'min': 4.2927019155779025, 'max': 4.824384586757542}\n",
      "-- Generation 13 --\n",
      "{'avg': 4.551326910101, 'std': 0.08946040729400498, 'min': 4.372071320583641, 'max': 4.824384586757542}\n",
      "-- Generation 14 --\n",
      "{'avg': 4.587589426314754, 'std': 0.0805423487585845, 'min': 4.500609963778544, 'max': 4.824384586757542}\n",
      "-- Generation 15 --\n",
      "{'avg': 4.602634555200605, 'std': 0.07493386528613176, 'min': 4.56160461141991, 'max': 4.824384586757542}\n",
      "-- Generation 16 --\n",
      "{'avg': 4.634444057356399, 'std': 0.0914606879122517, 'min': 4.56160461141991, 'max': 4.824384586757542}\n",
      "-- Generation 17 --\n",
      "{'avg': 4.664294775533885, 'std': 0.09030833761289975, 'min': 4.594284966578425, 'max': 4.824384586757542}\n",
      "-- Generation 18 --\n",
      "{'avg': 4.698916117197356, 'std': 0.08843544532723938, 'min': 4.594284966578425, 'max': 4.824384586757542}\n",
      "-- Generation 19 --\n",
      "{'avg': 4.751754563742116, 'std': 0.06807724730444412, 'min': 4.662084522410146, 'max': 4.841101830996571}\n",
      "-- Generation 20 --\n",
      "{'avg': 4.784958192944236, 'std': 0.06470731498804161, 'min': 4.690183026727174, 'max': 4.88505109788028}\n",
      "-- Generation 21 --\n",
      "{'avg': 4.827145592087234, 'std': 0.0120491990414541, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 22 --\n",
      "{'avg': 4.828086602083243, 'std': 0.013472008436623379, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 23 --\n",
      "{'avg': 4.828420946968023, 'std': 0.013582969120864792, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 24 --\n",
      "{'avg': 4.8296712136610775, 'std': 0.01517749107706926, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 25 --\n",
      "{'avg': 4.83199272632193, 'std': 0.017866662435471165, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 26 --\n",
      "{'avg': 4.838170882657005, 'std': 0.0225520964775183, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 27 --\n",
      "{'avg': 4.8466830957467995, 'std': 0.025239326614584626, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 28 --\n",
      "{'avg': 4.858494767964053, 'std': 0.02348250369554849, 'min': 4.824384586757542, 'max': 4.88505109788028}\n",
      "-- Generation 29 --\n",
      "{'avg': 4.868399161217937, 'std': 0.020691170925185548, 'min': 4.841101830996571, 'max': 4.88505109788028}\n",
      "-- Generation 30 --\n",
      "{'avg': 4.87969573463591, 'std': 0.013093734894375802, 'min': 4.841101830996571, 'max': 4.88505109788028}\n",
      "-- Generation 31 --\n",
      "{'avg': 4.885467104720973, 'std': 0.002912047884849116, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 32 --\n",
      "{'avg': 4.885883111561665, 'std': 0.0040760179568179195, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 33 --\n",
      "{'avg': 4.886299118402359, 'std': 0.004939807536933373, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 34 --\n",
      "{'avg': 4.886715125243051, 'std': 0.00564299133763105, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 35 --\n",
      "{'avg': 4.887131132083743, 'std': 0.006240102610390962, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 36 --\n",
      "{'avg': 4.887755142344783, 'std': 0.006995226463275857, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 37 --\n",
      "{'avg': 4.889419169707554, 'std': 0.008472150644962017, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 38 --\n",
      "{'avg': 4.89191521075171, 'std': 0.009780585359699315, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 39 --\n",
      "{'avg': 4.895243265477252, 'std': 0.010398090775069778, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 40 --\n",
      "{'avg': 4.901899374928337, 'std': 0.008159993002805569, 'min': 4.88505109788028, 'max': 4.905851439914916}\n",
      "-- Generation 41 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 42 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 43 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 44 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 45 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 46 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 47 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 48 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 49 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 50 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 51 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 52 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 53 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 54 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 55 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 56 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 57 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 58 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 59 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 60 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 61 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 62 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 63 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 64 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 65 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 66 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 67 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 68 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 69 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 70 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 71 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 72 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 73 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 74 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 75 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 76 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 77 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 78 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 79 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 80 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 81 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 82 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 83 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 84 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 85 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 86 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 87 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 88 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 89 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 90 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 91 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 92 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 93 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 94 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 95 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 96 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 97 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 98 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 99 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n",
      "-- Generation 100 --\n",
      "{'avg': 4.905851439914916, 'std': 0.0, 'min': 4.905851439914916, 'max': 4.905851439914916}\n"
     ]
    }
   ],
   "source": [
    "#　初期個体（なければcolumnsだけ定義)\n",
    "cols = ['a', 'b','c', 'd', 'e']\n",
    "df = pd.DataFrame(columns=cols)\n",
    "ga = GenericAlgorithm(df)\n",
    "\n",
    "\n",
    "def func(x):\n",
    "    return sum(x)\n",
    "weights = (1.0, )\n",
    "ga.set_func([func], weights)\n",
    "\n",
    "\n",
    "lower = [0, 0, 0, 0, 0]\n",
    "upper = [1, 1, 1, 1, 1]\n",
    "ga.set_lim(lower, upper)\n",
    "\n",
    "\n",
    "n_population = 100\n",
    "n_generation = 100\n",
    "p_crossover = 0.5\n",
    "p_mutation = 0.1\n",
    "results = ga.run(n_population, n_generation, p_crossover, p_mutation)"
   ]
  },
  {
   "source": [
    "## 結果の可視化\n",
    "### 最適化されたx"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "for result in results:\n",
    "    plt.plot(result[:-1])\n",
    "plt.ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "どの結果も1にちかく、one max problemが解かれていることがわかる"
   ]
  },
  {
   "source": [
    "### 最適化されたy"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "r_hist = [r[-1] for r in results]\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(r_hist)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "最大値はxの次元数の5だが、5になっている"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}