GeneticAlgorithm.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Erickrus/leetcode/blob/master/GeneticAlgorithm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmWFeE7SIs2L"
      },
      "source": [
        "# 遗传算法 (Genetic Algorithm, GA)\n",
        "\n",
        "<img src='https://cdn.savemyexams.co.uk/cdn-cgi/image/w=1920,f=auto/uploads/2020/08/Lamarck-theory-of-evolution.png' width=400px/>\n",
        "\n",
        "## 定义\n",
        "**遗传算法** 是计算数学中用于解决最优化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等等。\n",
        "\n",
        "遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）可抽象表示为染色体，使种群向更好的解进化。\n",
        "\n",
        "## 算法\n",
        "本文中的遗传算法，是基于染色体的完全模拟。本文算法实现主要包括：遗传、突变、自然选择。为了简化问题帮助理解，本文未实现杂交\n",
        "\n",
        "**算法描述**\n",
        "* 初始化人口\n",
        "* 循环\n",
        "  * 评价种群中的个体适应度\n",
        "  * 选择产生下一个种群\n",
        "  * 改变该种群（变异）\n",
        "  * 直到停止循环的条件满足\n",
        "\n",
        "**GA参数**\n",
        "* 种群规模（populationSize）：即种群中染色体个体的数目\n",
        "* 基因长度（geneSize）：个体中染色体的长度\n",
        "* 突变概率（mutationProb）：控制着突变算子的使用频率\n",
        "* 中止条件（termination criteria）\n",
        "\n",
        "**基因表示**\n",
        "\n",
        "> ACGT 是 DNA 分子中四种碱基的首字母缩写词：腺嘌呤 (A, adenine)、胞嘧啶 (C, cytosine)、鸟嘌呤 (G, guanine) 和胸腺嘧啶 (T, thymine)。 DNA总是以这种方式配对: A-T, T-A, C-G, G-C。\n",
        "\n",
        "> 本文中的DNA链长40位，每一位表示一个4进制数，这样可以分别表示4个10位的4进制数。\n",
        "\n",
        "\n",
        "```\n",
        "GAGCGGTCCG GCCCCAGCCT CGTATGGCCA GGCTGTTTTA \n",
        "|||||||||| |||||||||| |||||||||| |||||||||| \n",
        "CTCGCCAGGC CGGGGTCGGA GCATACCGGT CCGACAAAAT \n",
        "\n",
        "2023221332 2333302331 3210122330 2231211110 \n",
        "    776930     517118     256283      87674\n",
        "```\n",
        "\n",
        "**优化函数** \n",
        "\n",
        "> 本文中的优化函数，也就是**自然选择**得分函数，采用的是$negative L2 loss$，$v_i$是基因表达的数值，$t_i$是优化目标，其具体形式如下：\n",
        "\n",
        "> $score = -\\displaystyle\\sum^{3}_{i=0}(log_{10}{v_i} - t_i)^2$\n",
        "\n",
        "> 最大化$score$，就等价于将每个分量$(log_{10}{v_i} - t_i)^2$优化至最小，使的$log_{10}{v_i}$趋近于$t_i$"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 遗传基因模拟"
      ],
      "metadata": {
        "id": "nEeG_mPL7JHr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bl5TE86-Ioj2",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "#@title 遗传基因定义\n",
        "import gdown\n",
        "import os\n",
        "import random\n",
        "import copy\n",
        "\n",
        "from PIL import Image, ImageDraw\n",
        "\n",
        "class Gene:\n",
        "  def __init__(self):\n",
        "    self.geneImage = \"gene.png\"\n",
        "    # 这里假定DNA链长度为40, 可以表示4个不同的基因\n",
        "    self.geneSize = 40 #@markdown * 基因长度（geneSize）：个体中染色体的长度\n",
        "    self.parentId = -1\n",
        "    self.id = 0\n",
        "    # 一开始的时候会随机生成一组DNA链\n",
        "    self.gene = [ self._random_dna_molecule() for i in range(self.geneSize)]\n",
        "    if not os.path.exists(self.geneImage):\n",
        "      self._get_gene_png()\n",
        "\n",
        "  # 下载基因显示图片模版\n",
        "  def _get_gene_png(self):\n",
        "    os.system('gdown https://www.genome.gov/sites/default/files/tg/en/illustration/acgt.jpg')\n",
        "    im = Image.open(\"acgt.jpg\")\n",
        "    im = im.resize((800,396))\n",
        "    im = im.crop((0,30,800,336))\n",
        "    im.save(self.geneImage)\n",
        "    os.system(\"rm acgt.jpg\")\n",
        "\n",
        "  # 返回一个随机的DNA配对\n",
        "  def _random_dna_molecule(self):\n",
        "    return ['A-T','T-A','C-G','G-C'][random.randint(0,3)]\n",
        "  \n",
        "  # 展现一下DNA\n",
        "  def visualize(self):\n",
        "    im = Image.open(self.geneImage)\n",
        "    draw = ImageDraw.Draw(im)\n",
        "    draw.rectangle([(132,226+5), (670,263+5)], fill=(255,255,255))\n",
        "    moleculeColors = {'A':(64,183,67),'T':(255,12,18),'G':(0,0,0),'C':(120,169,220)}\n",
        "    for i in range(len(self.gene)):\n",
        "      dnaMolecule = self.gene[i]\n",
        "      draw.text((133 + int(i*13.5), 233), dnaMolecule[0], fill=moleculeColors[dnaMolecule[0]])\n",
        "      draw.text((133 + int(i*13.5), 253), dnaMolecule[2], fill=moleculeColors[dnaMolecule[2]])\n",
        "    return im.resize((600,230)) # 精简一下显示尺寸\n",
        "  \n",
        "  # 将DNA转化为数值\n",
        "  def to_array(self):\n",
        "    dna = [0,0,0,0]\n",
        "    for i in range(len(self.gene)):\n",
        "      unit = i // 10\n",
        "      p = i % 10\n",
        "      dnaMolecule = self.gene[i][0]\n",
        "      moleculeValues = {'A':0,'T':1,'G':2,'C':3}\n",
        "      dna[unit] += moleculeValues[dnaMolecule] * int(4 ** p)\n",
        "    return dna\n",
        "\n",
        "  def __str__(self):\n",
        "    geneText = [\"\",\"\",\"\",\"\",\"\",\"\"]\n",
        "\n",
        "    for i in range(len(self.gene)):\n",
        "      geneText[0] += self.gene[i][0]\n",
        "      geneText[1] += '|'\n",
        "      geneText[2] += self.gene[i][2]\n",
        "      geneText[4] += {'A':'0','T':'1','G':'2','C':'3'}[self.gene[i][0]]\n",
        "      if i%10 ==9 and i>0:\n",
        "        geneText[0] += ' '\n",
        "        geneText[1] += ' '\n",
        "        geneText[2] += ' '\n",
        "        geneText[4] += ' '\n",
        "    values = self.to_array()\n",
        "    geneText[5] = \" \".join([\"%10d\" for v in values]) % tuple(values)\n",
        "    geneText = \"\\n\".join(geneText)\n",
        "    geneText = \"id: %d, parentId: %d\\n\" %(self.id, self.parentId)  + geneText\n",
        "    return geneText\n",
        "\n",
        "  # 产生变异\n",
        "  def mutate(self):\n",
        "    mutatePosition = random.randint(0, 39)\n",
        "    self.gene[mutatePosition] = self._random_dna_molecule()\n",
        "    return mutatePosition\n",
        "  \n",
        "  # 繁殖后代\n",
        "  def born(self, bornId, mutationCount=0):\n",
        "    g = Gene()\n",
        "    # 注 这里需要deepcopy 否则数据会错乱\n",
        "    g.gene = copy.deepcopy(self.gene)\n",
        "    g.parentId = self.id # 记录父节点id\n",
        "    g.id = bornId        # 获得下一轮id\n",
        "    for i in range(mutationCount): # 根据mutationCount进行变异\n",
        "      g.mutate()\n",
        "    return g\n",
        "\n",
        "g = Gene()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "EDGXV3JYVwap",
        "outputId": "e93a0e04-486f-49d6-be78-3a13fee92c1f",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id: 0, parentId: -1\n",
            "GCAAGAAACG AATGCTCAAT GGTCTGACTA TCAAAGCAGA \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "CGTTCTTTGC TTACGAGTTA CCAGACTGAT AGTTTCGTCT \n",
            "\n",
            "2300200032 0012313001 2213120310 1300023020 \n",
            "    721422     276368     117210     145421\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=600x230 at 0x7FF01A0844D0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 157
        }
      ],
      "source": [
        "#@title ###显示基因信息\n",
        "print(g)\n",
        "g.visualize()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "MASVVRvLVT8J",
        "outputId": "34bb93d6-dfca-497d-a70a-293d3e7f46ee",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mutation position: T-A at 13\n",
            "GATAGCATGG GCATATCCTA ACCGGTGGGA CGGGTACGAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "CTATCGTACC CGTATAGGAT TGGCCACCCT GCCCATGCTA \n",
            "\n",
            "2010230122 2301013310 0332212220 3222103201 \n",
            "    675346     128078     173756     307627\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=600x230 at 0x7FF019C5C610>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 136
        }
      ],
      "source": [
        "#@title 基因变异，随机改变其中一个位置\n",
        "mutationPosition = g.mutate()\n",
        "print(\"mutation position: %s at %d\" % (g.gene[mutationPosition], mutationPosition))\n",
        "print(g)\n",
        "g.visualize()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##自然环境模拟"
      ],
      "metadata": {
        "id": "izochfM77TqU"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 209,
      "metadata": {
        "id": "djBgD8yqZwML",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "from typing import Generator\n",
        "#@title 模拟自然环境定义, 自然选择设定\n",
        "import json\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import random\n",
        "import tqdm\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "from IPython import display\n",
        "\n",
        "\n",
        "\n",
        "class World:\n",
        "  def __init__(self, environmentChange=False, convergeAndStop=True):\n",
        "    self.maxEpochs = 1000         # 总迭代次数\n",
        "    self.populationSize = 5000    # @markdown * 种群规模（populationSize）：即种群中染色体个体的数目\n",
        "    self.elitePopulationSize = 10 # 精英人口数\n",
        "    self.winnerTakesAllNum = 20   # 赢者通吃系数\n",
        "    self.mutationCount = 1        # @markdown * 突变概率（mutationProb）：控制着突变算子的使用频率\n",
        "    self.mutationProb = 0.001     # 变异概率\n",
        "    self.lastEpoch = -1           # 当前迭代数\n",
        "\n",
        "    self.stats = []               # 各时代的总体统计量\n",
        "    self.eliteStats = []          # 各时代精英体统计量\n",
        "\n",
        "    self.populations = self.init_populations() # 人口数\n",
        "    self.targetNames = [\"speed\", \"size\", \"weight\", \"power\"] # 优化目标名称\n",
        "    \n",
        "    self.environmentChange = environmentChange\n",
        "    self.convergeAndStop = convergeAndStop\n",
        "\n",
        "    # 随机优化目标\n",
        "    self.targets = [random.randint(0,6) for _ in range(4)]\n",
        "    self._show_optimization_targets()\n",
        "    \n",
        "\n",
        "  # 初始化每一代的数据\n",
        "  def _create_epoch_database(self):\n",
        "    os.system(\"rm -Rf epoch_database > /dev/null\")\n",
        "    os.system(\"mkdir epoch_database\")\n",
        "  \n",
        "  def save_epoch(self, epoch):\n",
        "    jsonFilename = os.path.join(\"epoch_database\", ((\"w%5d.json\")%epoch).replace(\" \",\"0\"))\n",
        "    data = {\n",
        "      \"epoch\": epoch,\n",
        "      \"populations\": [{\n",
        "        \"id\":self.populations[i].id, \n",
        "        \"parentId\":self.populations[i].parentId, \n",
        "        \"gene\": self.populations[i].gene\n",
        "      } for i in range(len(self.populations))],\n",
        "      \"targets\": self.targets,\n",
        "    }\n",
        "    with open(jsonFilename, \"w\") as f:\n",
        "      f.write(json.dumps(data, indent=2, ensure_ascii=False))\n",
        "\n",
        "  def lineage_analysis(self, startEpoch, id):\n",
        "    genes = []\n",
        "    currId = id\n",
        "    for epoch in reversed(range(startEpoch+1)):\n",
        "      jsonFilename = os.path.join(\"epoch_database\", ((\"w%5d.json\")%epoch).replace(\" \",\"0\"))\n",
        "      with open(jsonFilename, \"r\") as f:\n",
        "        data = json.loads(f.read())\n",
        "      g = Gene()\n",
        "      g.id = data[\"populations\"][currId][\"id\"]\n",
        "      g.parentId = data[\"populations\"][currId][\"parentId\"]\n",
        "      g.gene = data[\"populations\"][currId][\"gene\"]\n",
        "      targets = data[\"targets\"]\n",
        "      genes.append({\"epoch\": epoch, \"gene\": g, \"targets\": targets})\n",
        "      currId = g.parentId\n",
        "    for i in range(len(genes)):\n",
        "      print(\"epoch: %d\" % genes[i][\"epoch\"])\n",
        "      print(str(genes[i][\"gene\"]))\n",
        "      if i < len(genes)-1:\n",
        "        if str(genes[i][\"targets\"]) != str(genes[i+1][\"targets\"]):\n",
        "          print(\"optimization targets: %s\" % str(genes[i][\"targets\"]))\n",
        "        if genes[i][\"gene\"].gene != genes[i+1][\"gene\"].gene:\n",
        "          print(\"mutated\")\n",
        "      if i ==len(genes)-1:\n",
        "        print(\"optimization targets: %s\" % str(genes[i][\"targets\"]))\n",
        "\n",
        "      print(\"\\n\")\n",
        "          \n",
        "\n",
        "\n",
        "  # 根据优化目标进行选择\n",
        "  def natural_select(self, epoch, animal):\n",
        "    values = animal.to_array()\n",
        "    # negative L2 loss\n",
        "    score = 0.\n",
        "    for i in range(len(values)):\n",
        "      score += -(math.log(values[i]+1)/math.log(10) - self.targets[i])**2\n",
        "    return score\n",
        "\n",
        "  #@markdown 初始化人口\n",
        "  def init_populations(self):\n",
        "    populations = []\n",
        "    for id in range(self.populationSize):\n",
        "      # 这里的基因是完全随机的\n",
        "      animal = Gene()\n",
        "      animal.id = id\n",
        "      populations.append(animal)\n",
        "    return populations\n",
        "\n",
        "  # 通过随机获得变异次数，不是每次都会变异\n",
        "  def _get_random_mutation_count(self, prob=0.05):\n",
        "    mutationCount = 0\n",
        "    if random.randint(0, int(1./prob)) == 0:\n",
        "      mutationCount= random.randint(1,self.mutationCount)\n",
        "    return mutationCount\n",
        "  \n",
        "  # 普通群体和精英群体差异小于阈值则表示收敛\n",
        "  def is_converge(self, threshold=0.01):\n",
        "    return abs(self.stats[-1] - self.eliteStats[-1]) <= threshold\n",
        "\n",
        "  def _show_optimization_targets(self):\n",
        "    fmt = \"optimization target \"+\", \".join([\"%s: %s\"%(self.targetNames[i], \"%d\") for i in range(len(self.targetNames))])\n",
        "    print(fmt % tuple(self.targets))\n",
        "\n",
        "  def _show_progress(self, epoch):\n",
        "    plt.plot(np.array(self.stats))\n",
        "    plt.plot(np.array(self.eliteStats))\n",
        "    # 普通群体，精英群体\n",
        "    plt.legend(['General Score', 'Elite Score'])\n",
        "\n",
        "    # 清屏，重新绘制\n",
        "    display.clear_output(wait=True)\n",
        "    print(\"epoch %d/%d\" % (epoch, self.maxEpochs))\n",
        "    self._show_optimization_targets()\n",
        "\n",
        "    values = self.populations[0].to_array()\n",
        "    score = self.natural_select(0, self.populations[0])\n",
        "\n",
        "    print(\"%6s: %1.4f\" % (\"score\", score))\n",
        "    for i in range(len(self.targets)):\n",
        "       print( \"%6s: %1.4f %1.4f\"%(self.targetNames[i], math.log(values[i]+1)/math.log(10),  float(self.targets[i])))\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "  # 模拟运行，也就是启动物尽天择\n",
        "  def simulate(self):\n",
        "    self._create_epoch_database()\n",
        "\n",
        "    #@markdown 循环\n",
        "    for epoch in tqdm.tqdm(range(self.maxEpochs)):\n",
        "      #@markdown > 评价种群中的个体适应度\n",
        "      scores = [] # 计算当前世代的各生命体的得分情况, 这个得分是基于自然选择的结果\n",
        "      for id in range(len(self.populations)):\n",
        "        animal = self.populations[id]\n",
        "        scores.append({\"id\": id, \"score\": self.natural_select(epoch, animal)})\n",
        "      # 根据自然选择的结果，进行排序，其中分数得分高的排在较上的位置\n",
        "      # 由于自然选择中的函数选择为 negative L2 loss，所以得分越高，表示越好\n",
        "      sortedScores = list(reversed(sorted(scores, key=lambda x: x[\"score\"])))\n",
        "\n",
        "      # 计算统计量\n",
        "      currScore = np.array([s[\"score\"] for s in sortedScores])       # 根据自然选择得分排序后的得分\n",
        "      self.stats.append(currScore.mean())                            # 普通群体平均值\n",
        "      self.eliteStats.append(currScore[:self.elitePopulationSize].mean()) # 精英群体平均值\n",
        "\n",
        "      #@markdown > 选择产生下一个种群\n",
        "      # 以下部分，根据得分繁殖下一代\n",
        "      nextGenerationPopulations = []\n",
        "      bornId = 0 # 为每个个体分配一个出生id\n",
        "      for i in range(len(sortedScores)):\n",
        "        # 对于得分高的精英群体，给予奖励，会产生更多的后代\n",
        "        # 以下公式含义: 对于排名越靠前的获得奖励越多\n",
        "        # 前10名 分别可以增加: 20 * 1, 20 * 0.9, 20 * 0.8 ...\n",
        "        if i <= self.elitePopulationSize-1:\n",
        "          for j in range(int(self.winnerTakesAllNum * (self.elitePopulationSize-i)/float(self.elitePopulationSize))):\n",
        "          #@markdown > 改变该种群（变异）\n",
        "            nextGenerationPopulations.append(\n",
        "              self.populations[sortedScores[i][\"id\"]].born(\n",
        "                bornId = bornId,\n",
        "                mutationCount=self._get_random_mutation_count(prob=self.mutationProb)\n",
        "              )\n",
        "            )\n",
        "            bornId += 1\n",
        "        else:\n",
        "          nextGenerationPopulations.append(\n",
        "            self.populations[sortedScores[i][\"id\"]].born(\n",
        "              bornId = bornId,\n",
        "              mutationCount=self._get_random_mutation_count(prob=self.mutationProb)\n",
        "            )\n",
        "          )\n",
        "          bornId += 1\n",
        "        # 那么对于排名靠后的，就被选择淘汰了。此处如果排名靠后就不会产生后代\n",
        "        # 为了便于计算，此处保持人口总数不变\n",
        "        if len(nextGenerationPopulations) >= self.populationSize:\n",
        "          break\n",
        "      \n",
        "      self.save_epoch(epoch)\n",
        "      self.lastEpoch = epoch\n",
        "      # 下一代了, 上一代 R.I.P\n",
        "      self.populations = nextGenerationPopulations[:self.populationSize]\n",
        "\n",
        "      if epoch % 10 == 0 and epoch>0: # 每10代，显示一次当前情况\n",
        "        self._show_progress(epoch)\n",
        "\n",
        "      if self.environmentChange and random.randint(0,75) == 0: # 假设环境变了，优化目标发生改变？环境变了？\n",
        "        self.targets = [random.randint(0,6) for _ in range(4)]\n",
        "      #@markdown > 直到停止循环的条件满足\n",
        "      if self.convergeAndStop and self.is_converge(0.1): # 如果提前收敛，则训练结束\n",
        "        break\n",
        "\n",
        "    self._show_progress(epoch)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 模拟自然选择的过程\n",
        "#@markdown 是否允许环境变化:\n",
        "environmentChange = True #@param {type:\"boolean\"}\n",
        "#@markdown 收敛后是否终止执行:\n",
        "convergeAndStop = True #@param {type:\"boolean\"}\n",
        "world = World(environmentChange, convergeAndStop)\n",
        "world.simulate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "cellView": "form",
        "id": "V_xj9FPd_9zs",
        "outputId": "9ca34b50-c081-4a27-92ca-d1b673a9742b"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 123/1000\n",
            "optimization target speed: 3, size: 0, weight: 6, power: 6\n",
            " score: -0.0333\n",
            " speed: 3.0766 3.0000\n",
            "  size: 0.0000 0.0000\n",
            "weight: 5.8501 6.0000\n",
            " power: 5.9293 6.0000\n"
          ]
        },
        {
          "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",
      "execution_count": 211,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "WjzlhbbBSMy1",
        "outputId": "cbd1c00c-87ce-454d-a881-770239209cb4",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "populationId: 3875/4999\n",
            "score: -0.2728\n",
            "optimization target ~ speed: 3.0000, size: 0.0000, weight: 6.0000, power: 6.0000\n",
            "optimization result ~ speed: 3.0777, size: 0.0000, weight: 5.8295, power: 5.5125\n",
            "id: 3875, parentId: 4819\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=600x230 at 0x7FF01861E3D0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 211
        }
      ],
      "source": [
        "#@title 显示最终的DNA以及对应的参数\n",
        "#@markdown 根据编号查询 (0-4999):\n",
        "populationId = 3875 #@param {type:\"slider\", min:0, max:4999, step:1}\n",
        "print(\"populationId: %d/%d\" % (populationId, world.populationSize-1))\n",
        "print(\"score: %1.4f\" % world.natural_select(0, world.populations[populationId]))\n",
        "fmt = \"optimization target ~ \"+\", \".join([\"%s: %s\"%(world.targetNames[i], \"%1.4f\") for i in range(len(world.targetNames))])\n",
        "print(fmt % tuple(\n",
        "    world.targets\n",
        "))\n",
        "fmt = \"optimization result ~ \"+\", \".join([\"%s: %s\"%(world.targetNames[i], \"%1.4f\") for i in range(len(world.targetNames))])\n",
        "print(fmt % tuple(\n",
        "    np.log(np.array(world.populations[populationId].to_array())+1.)/np.log(10.)\n",
        "))\n",
        "print(world.populations[populationId])\n",
        "world.populations[populationId].visualize()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title 血缘分析\n",
        "#@markdown 根据编号查询 (0-4999):\n",
        "populationId = 885 #@param {type:\"slider\", min:0, max:4999, step:1}\n",
        "world.lineage_analysis(world.lastEpoch, populationId)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "5Yvyw1ow-BsJ",
        "outputId": "7b617159-0a2f-4eee-cc50-d2b33f71d03d"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch: 123\n",
            "id: 885, parentId: 550\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 122\n",
            "id: 550, parentId: 687\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 121\n",
            "id: 687, parentId: 350\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 120\n",
            "id: 350, parentId: 487\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 119\n",
            "id: 487, parentId: 151\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 118\n",
            "id: 151, parentId: 369\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 117\n",
            "id: 369, parentId: 50\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 116\n",
            "id: 50, parentId: 217\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 115\n",
            "id: 217, parentId: 2\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 114\n",
            "id: 2, parentId: 19\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 113\n",
            "id: 19, parentId: 1510\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "\n",
            "\n",
            "epoch: 112\n",
            "id: 1510, parentId: 2211\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCGC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGCG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313323 \n",
            "      1195          0     675342     980822\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 111\n",
            "id: 2211, parentId: 1310\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 110\n",
            "id: 1310, parentId: 2012\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 109\n",
            "id: 2012, parentId: 1112\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 108\n",
            "id: 1112, parentId: 1814\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 107\n",
            "id: 1814, parentId: 914\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 106\n",
            "id: 914, parentId: 1617\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 105\n",
            "id: 1617, parentId: 714\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 104\n",
            "id: 714, parentId: 1418\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 103\n",
            "id: 1418, parentId: 514\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 102\n",
            "id: 514, parentId: 1219\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 101\n",
            "id: 1219, parentId: 316\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 100\n",
            "id: 316, parentId: 1020\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 99\n",
            "id: 1020, parentId: 116\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 98\n",
            "id: 116, parentId: 902\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 97\n",
            "id: 902, parentId: 15\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 96\n",
            "id: 15, parentId: 717\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 95\n",
            "id: 717, parentId: 0\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 94\n",
            "id: 0, parentId: 519\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 93\n",
            "id: 519, parentId: 0\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 92\n",
            "id: 0, parentId: 319\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 91\n",
            "id: 319, parentId: 0\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 90\n",
            "id: 0, parentId: 119\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 89\n",
            "id: 119, parentId: 0\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 88\n",
            "id: 0, parentId: 371\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "\n",
            "\n",
            "epoch: 87\n",
            "id: 371, parentId: 961\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAC \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTG \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313303 \n",
            "      1195          0     675342     849750\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 86\n",
            "id: 961, parentId: 254\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 85\n",
            "id: 254, parentId: 862\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 84\n",
            "id: 862, parentId: 155\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 83\n",
            "id: 155, parentId: 763\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 82\n",
            "id: 763, parentId: 55\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 81\n",
            "id: 55, parentId: 615\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 80\n",
            "id: 615, parentId: 3\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 79\n",
            "id: 3, parentId: 419\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 78\n",
            "id: 419, parentId: 0\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 77\n",
            "id: 0, parentId: 219\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 76\n",
            "id: 219, parentId: 0\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 75\n",
            "id: 0, parentId: 19\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 74\n",
            "id: 19, parentId: 591\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "\n",
            "\n",
            "epoch: 73\n",
            "id: 591, parentId: 146\n",
            "CGGGATAAAA AAAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TTTTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0000000000 2300230122 2111313301 \n",
            "      1195          0     675342     325462\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 72\n",
            "id: 146, parentId: 392\n",
            "CGGGATAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0100000000 2300230122 2111313301 \n",
            "      1195          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 71\n",
            "id: 392, parentId: 27\n",
            "CGGGATAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0100000000 2300230122 2111313301 \n",
            "      1195          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 70\n",
            "id: 27, parentId: 218\n",
            "CGGGATAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0100000000 2300230122 2111313301 \n",
            "      1195          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 69\n",
            "id: 218, parentId: 1\n",
            "CGGGATAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0100000000 2300230122 2111313301 \n",
            "      1195          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 68\n",
            "id: 1, parentId: 19\n",
            "CGGGATAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0100000000 2300230122 2111313301 \n",
            "      1195          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 67\n",
            "id: 19, parentId: 2071\n",
            "CGGGATAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0100000000 2300230122 2111313301 \n",
            "      1195          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 66\n",
            "id: 2071, parentId: 1066\n",
            "CGGGATAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTATTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222010000 0100000000 2300230122 2111313301 \n",
            "      1195          4     675342     325462\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 65\n",
            "id: 1066, parentId: 1871\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 64\n",
            "id: 1871, parentId: 866\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 63\n",
            "id: 866, parentId: 1672\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 62\n",
            "id: 1672, parentId: 664\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 61\n",
            "id: 664, parentId: 1472\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 60\n",
            "id: 1472, parentId: 464\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 59\n",
            "id: 464, parentId: 1273\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 58\n",
            "id: 1273, parentId: 264\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 57\n",
            "id: 264, parentId: 1074\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 56\n",
            "id: 1074, parentId: 64\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 55\n",
            "id: 64, parentId: 917\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 54\n",
            "id: 917, parentId: 2\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 53\n",
            "id: 2, parentId: 719\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 52\n",
            "id: 719, parentId: 0\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 51\n",
            "id: 0, parentId: 519\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 50\n",
            "id: 519, parentId: 0\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 49\n",
            "id: 0, parentId: 319\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 48\n",
            "id: 319, parentId: 0\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 47\n",
            "id: 0, parentId: 119\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 46\n",
            "id: 119, parentId: 0\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 45\n",
            "id: 0, parentId: 316\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "\n",
            "\n",
            "epoch: 44\n",
            "id: 316, parentId: 3\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTCCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAGGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111313301 \n",
            "       171          4     675342     325462\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 43\n",
            "id: 3, parentId: 119\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111311301 \n",
            "       171          4     675342     317270\n",
            "\n",
            "\n",
            "epoch: 42\n",
            "id: 119, parentId: 0\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111311301 \n",
            "       171          4     675342     317270\n",
            "\n",
            "\n",
            "epoch: 41\n",
            "id: 0, parentId: 112\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111311301 \n",
            "       171          4     675342     317270\n",
            "\n",
            "\n",
            "epoch: 40\n",
            "id: 112, parentId: 207\n",
            "CGGGAAAAAA ATAAAAAAAA GCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT CGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 2300230122 2111311301 \n",
            "       171          4     675342     317270\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 39\n",
            "id: 207, parentId: 12\n",
            "CGGGAAAAAA ATAAAAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 1300230122 2111311301 \n",
            "       171          4     675341     317270\n",
            "\n",
            "\n",
            "epoch: 38\n",
            "id: 12, parentId: 19\n",
            "CGGGAAAAAA ATAAAAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 1300230122 2111311301 \n",
            "       171          4     675341     317270\n",
            "\n",
            "\n",
            "epoch: 37\n",
            "id: 19, parentId: 89\n",
            "CGGGAAAAAA ATAAAAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 1300230122 2111311301 \n",
            "       171          4     675341     317270\n",
            "\n",
            "\n",
            "epoch: 36\n",
            "id: 89, parentId: 315\n",
            "CGGGAAAAAA ATAAAAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTTTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100000000 1300230122 2111311301 \n",
            "       171          4     675341     317270\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 35\n",
            "id: 315, parentId: 4\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 34\n",
            "id: 4, parentId: 119\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 33\n",
            "id: 119, parentId: 0\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 32\n",
            "id: 0, parentId: 3144\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 31\n",
            "id: 3144, parentId: 3045\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "optimization targets: [3, 0, 6, 6]\n",
            "\n",
            "\n",
            "epoch: 30\n",
            "id: 3045, parentId: 2945\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 29\n",
            "id: 2945, parentId: 2847\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 28\n",
            "id: 2847, parentId: 2747\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 27\n",
            "id: 2747, parentId: 2647\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 26\n",
            "id: 2647, parentId: 2547\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 25\n",
            "id: 2547, parentId: 1297\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "\n",
            "\n",
            "epoch: 24\n",
            "id: 1297, parentId: 2061\n",
            "CGGGAAAAAA ATAAGAAAAA TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTT AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200000 1300230122 2111311301 \n",
            "       171        516     675341     317270\n",
            "mutated\n",
            "\n",
            "\n",
            "epoch: 23\n",
            "id: 2061, parentId: 1097\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 22\n",
            "id: 1097, parentId: 1862\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 21\n",
            "id: 1862, parentId: 896\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 20\n",
            "id: 896, parentId: 1662\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 19\n",
            "id: 1662, parentId: 695\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 18\n",
            "id: 695, parentId: 1462\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 17\n",
            "id: 1462, parentId: 494\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 16\n",
            "id: 494, parentId: 1263\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 15\n",
            "id: 1263, parentId: 292\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 14\n",
            "id: 292, parentId: 1064\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 13\n",
            "id: 1064, parentId: 153\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 12\n",
            "id: 153, parentId: 964\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 11\n",
            "id: 964, parentId: 53\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 10\n",
            "id: 53, parentId: 816\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 9\n",
            "id: 816, parentId: 2\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 8\n",
            "id: 2, parentId: 619\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 7\n",
            "id: 619, parentId: 0\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 6\n",
            "id: 0, parentId: 419\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 5\n",
            "id: 419, parentId: 0\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 4\n",
            "id: 0, parentId: 219\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 3\n",
            "id: 219, parentId: 0\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 2\n",
            "id: 0, parentId: 19\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 1\n",
            "id: 19, parentId: 1644\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "\n",
            "\n",
            "epoch: 0\n",
            "id: 1644, parentId: -1\n",
            "CGGGAAAAAA ATAAGAAAAC TCAAGCATGG GTTTCTTCAT \n",
            "|||||||||| |||||||||| |||||||||| |||||||||| \n",
            "GCCCTTTTTT TATTCTTTTG AGTTCGTACC CAAAGAAGTA \n",
            "\n",
            "3222000000 0100200003 1300230122 2111311301 \n",
            "       171     786948     675341     317270\n",
            "optimization targets: [0, 6, 5, 4]\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "authorship_tag": "ABX9TyOX1pU1IvXBsUMl+j7lVGID",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}