Lab Ass 3.1.4 (Real GA, Q4).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter the number of variables: 20\n",
      "Enter the initial population size: 10\n",
      "Enter the minimum value of X: -500\n",
      "Enter the maximum value of X: 500\n",
      "Enter the max number of iterations: 300\n",
      "[ -732.24038674  -732.24038674  -732.24038674  -864.71141812\n",
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      "  -879.59108056  -879.85718187  -880.15855878  -880.1930213\n",
      "  -880.24770263  -882.17420044  -882.48372788  -882.48372788\n",
      "  -883.53311838  -886.96602004  -888.28421197  -888.28421197\n",
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      "  -912.02054993  -912.03198618  -914.1886125   -914.91997169\n",
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      "  -963.46624264  -963.72845966  -964.70353663  -965.9114823\n",
      "  -964.66752001  -965.00530956  -967.428604    -967.428604\n",
      "  -970.33893919  -970.35436946  -970.94120272  -971.02829466\n",
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      "  -979.09928564  -979.48221855  -982.9437425   -983.03733486\n",
      "  -983.56737025  -984.77116172  -987.76654027  -987.76654027\n",
      "  -987.97006436  -989.5910501   -990.3894738   -991.68154754\n",
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      " -1118.42511416 -1118.54071323 -1118.54071323 -1118.65276625]\n"
     ]
    },
    {
     "data": {
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OP2avZXB+Nt8a2eugpjURkWiKWs0FGO7ux9Zaf8fM5jQ+tHr1ByYHy28BE4GfAmOB59x9H7DCzJYSTjQAS919OYCZPRfse8jkkuj6dgo3hQ3Oz6Z9RipTl2/hludm86s3FjN2SDduPLUP2W1S6jmLiEjzaEhyqTKzPu6+DMDMehNulmou84ALgFcID3vuEZTnA1Nr7VcSlAGsPqD8+EgnNrNxwDiAgoKC6EUcQ+kpIe6+YBDlldX88f1lzCnZwR/eW8bD7y+jTUqIUJJxUp+O3Hx6kR7SFJEW05Dk8gPgXTNbDhjQE7i2KR9qZpOALhE23QVcBzxgZj8DJgDlNYdF2N+J/CBoxLY+dx8PjIdws9hhhh3XUpOT+O6ZRQDMWb2dyZ9tYmdZBbv2VfHPuWuZOH8Dp/fP42vDezCsZw55mWkxjlhEjmT1Jhd3f9vMigg3VxmwKGiaajR3H13PLmcDmFk/4CtBWQlf1GIAugNrg+W6ylulY3u059ge7fev33neAP4y5XMe+3AF7y7eBMBxBe05b3BXxgzqQo+cjLpOJSLSKHV26AdTvJi7/+WA8huA3e7+TLMEZNbJ3TeaWRLwBPCeuz9uZoOAZ/iiQ/9toIhwwvsMOBNYQ7hD/0p3n3+oz0n0Dv3G2FdZxbw1O5m2YgvPfryK1Vv30iUrndduGUmHjBRqRjsnWfjZGxGRAzX5fS5mNgsY5e6lB5RnAe821/tczOwW4KZg9e/AnR4EaWY1zWaVwK3u/npQfh5wP+GhyI+7+z31fU5rTC61uTuzV2/na3+cSnlV9Ze2DS1oz/9ccgzdO2SQnhKKUYQiEo+ikVzmuvsxh7stUbT25FJjyrItTF2+BTMwjPKqKh7/cCV7K6rokJHC+cd0Iy05iVCScXR+Nl89tlusQxaRGIrGUOQUM2vr7rsPOHEmkNrUACU+nNinIyf2+fK8ZRcN7c4nK7fyxrz1/GPWGqrdqah2yiurWbV1D984oSft0pIJJanpTEQiO1TN5XbC/RjfcfeVQVkh8BDhfpD/aZkQm4dqLoensqqam56ZycT5G/aXDeyaxfM3nkBWup6nEWktmlxzcfdfm9ku4H0za0d4eO9u4F53fzh6oUoiSA4l8fDXh/Hxyq3MWb2dnWUVPPL+cm56eiYn9cmlV24GYwZ10UAAEQHqGYrs7o8AjwTJxQ7s3JfWJSnJOKF3R04Ipv/PSk/hvjcW8cGSzQCc0jeXwtwM0pJDpCUn0bNjBpcO60GSms9EWp0Gzdvu7ruaOxBJPDee2ocbRvamrLKKp6Z8zl+mfM6CdTvZV1FFWWU1VdVOeZVz1Qk9Yx2qiLSwBk25fyRSn0vzcne+8dg0pizbQo+cDB668jiOztf0MyKJrqF9LpGmTjnwRAfNExKpTKQ2M+O3XxvCt0/tQ0VlNZf9cQqXPTKFDTv1njmR1qDe5AJMaWCZyJd0ykznh+cM4OkbTuD8Y7oya/U27p/0Wf0HikjCq7PPxcy6EJ51uI2ZDeWLiSOzAE1GJQ3WK7ctv7rkWDJSk3niXyuZtHAjue3SOK1/HjeO6k37DD02JXKkOVSH/hjgm4QngvwNXySXUuDHzRuWHIm+f3Y/2meksH5HGSXb9jJ+8nL+Nr2Eok7tSE9JIjU5iZFFefTIyeD4XjmaekYkgTXkTZT/5u4vtVA8LUYd+rH3ackOfvf2EnbsLaesoprte8tZvXUvAIUdM/j5Vwdxar88DWUWiSPRfBNl92CyylLgT8BxwB3u/mYTY5RWbnD3bB695ovfUXdn2abdLNu0i/veWMS1T3xCekoS939tCOcc3TWGkYrI4WpIcrnO3X9nZmOAToRfFPZnQMlFosrM6NupHX07teP0/p2YMGctj36wnLtenkdacoi2ackM6Jqp6WZEEkBDkktNm8R5wJ/dfY5pjg9pZqnJSVwyrDsDu2Zy0R/+xbVPfAJAcpJx+5j+fPvUPjGOUEQOpSHJZYaZvQn0Au4MZkWurucYkagY1C2bD354Omu272XH3gqe+3gV976+iNc/XUd2MMosZHDBkG5cNLR7jKMVkRoN6dBPAoYAy919u5l1BPLdfW5LBNhc1KGfmCqrqnno3WVMW7GFPeVVOLBjTzkrt+yhW3Y6bVJDDM7P5vpTejO4u2YEEIm2aHboO3AUcD7wH0BbIL1p4dXNzI4FHgHaASuBr7v7zmC6/4XA4mDXqe7+7eCYYYRfidwGeA24xVvrvDZHuORQEreMLiL8huuwyqpqHv9oBYvX72LXvgreWbSRCXPW8pOvHMV1p/SKXbAirVhDkssfCDeDnUE4uZQCLwHDmymmR4Hb3f19M7sO+AHw02DbMncfEuGYh4FxwFTCyeUc4PVmik/iTHIoiXGjvuiD2VlWwfeen8MvX1tIx3apdMlKJyU5iYFdsmiTqmdnRFpCQ5LL8e5+nJnNAnD3bWbWnI9U9wcmB8tvARP5IrkcxMy6AlnuPiVYfwq4ECWXVisrPYXfXHosY+6fzC3Pzd5fPqBLJq/cfDJpyUowIs2tIcmlwsxChJvHMLM8mrdDfx5wAfAKcCnQo9a2XkGS2wn8xN0/IDxFTUmtfUqCMmnFsjNSmHjrKD7bWEp5ZTVLN+7i5xPmc9kjUzi1Xx43ntqHtmkNeuOEiDRCQ/51PQC8DHQ2s3uAS4CfNOVDzWwS0CXCpruA64AHzOxnwASgPNi2Dihw9y1BH8s/zGwQXwyVri1if4uZjSPcfEZBQUFTLkESQHZGCsMLcwA4uW8uu/ZV8uqctTzwzlLeXLCBF79zEu2UYESaRYPe52JmA4Azg9V33H1hs0b1xef2A/7q7iMibHsPuB1YA7zr7gOC8iuA09z9xkOdW6PFWq93Fm3ghqdm0CEjlbOO6sw9Fx6tKWZEGiiao8UgPAtyTdNYm6YEVh8z6+TuG4Mh0D8hPHKspjluq7tXmVlvwsOFlrv7VjMrNbMTgGnA1cCDzRmjJLYzBnTmoSuH8sL0Ep79eBU791ZQmJtBcWEOp/fvFOvwRI4IDXlZ2M+AJ4EcIBf4s5k1qVmsHleY2WfAImAt4almAEYBc81sDvAi8G133xps+w7hUWZLgWWoM1/qcc7RXXnsmmKuPrEnby3cwMPvLeNbT05nwdqdsQ5N5IjQkIcoFwJD3b0sWG8DzHT3gS0QX7NRs5jUtn1POWf+5n0qqqrJbZdGQccM7jx3IP27ZMY6NJG4Es1msZWEH5qseT9tGuHagcgRo31GKn+8ahjPTFtFeVU1Hy3dzFce+ICxQ/LpndeW4p4daJuWTM+OGWRq4kyReh3qTZQPEu5j2QfMN7O3gvWzgA9bJjyRllNcmENxMLps6+5y7nt9EW/MX8+OmRX790lLTuK6U3pxUp+OvL94EwO7ZnHxcfloLleRL6uzWczMrjnUge7+ZLNE1ELULCYNtal0HwvW7WRveSUT52/g5VlrAAglGVXVzk2n9+Hrx/ekW/tmHesiEhca2izWoKHIRyIlF2mseWt2MLdkBxcM6cZPXv6Uf8xeS5LBk9eNYGRRXqzDE2lWTU4uZjYW6O7uDwXr04Cafzk/dPcXoxVsLCi5SDRUVFUzZdkWfvHqfLbvqeD43jkM65lDjw5tOHNgZ0J6fkaOMNHo0P8hcHmt9TTCk1W2JTw8OKGTi0g0pISSGNUvjweuGMovXl3A3JIdvPbpegBO65/HN08q5DQ9OyOt0KGSS6q7r661/qG7bwG2mFnbZo5LJKEM6pbNCzeeiLuzadc+Xpu7jl++toj3Fm/i0auLGX1U51iHKNKiDvUQZYfaK+5+c61VNSyLRGBmdMpM55sn92LWz84ip20qE+asjXVYIi3uUMllmpndcGChmd0IfNx8IYkcGdqmJTNmUBcmLdzAB0s2MXPVNnbsraj/QJEjwKGaxW4jPPPwlcDMoGwY4b6XC5s7MJEjwYVDuvHsx6u46rHw/8dSQsb4q4s1h5kc8Roy/csZwKBgdb67v9PsUbUAjRaTlrJ0Yynb9lSwc28Fv3xtIZXVzu8uH8qALpmkp+jFZZJYojb9S5BMjoiEIhILfTt9MT9ZanISVz32MRc+9BFpyUl886RCbjurn5KMHHH0piSRFjSyKI+3bhvFsk27eXP+ev44eTml+yr55UWDYx2aSFQpuYi0sKLOmRR1zuSco7vQsV0qf/pgBUs37iK7TQrXnlTISX1zYx2iSJPV+z4XEWk+3z+7P1eM6AHApyU7+MZj07jjpbl8vmV3jCMTaRrVXERiKD0lxH9ffAwAu/ZV8t+vLeSlmSW8PGsNt47ux7dG9iIlpP8DSuLRxJUicWb9jjJ+9so83lywgYFdszhnUBcuGppPQceMWIcm0uDRYjH5L5GZXWpm882s2syKD9h2p5ktNbPFZjamVvk5QdlSM7ujVnkvM5tmZkvM7HkzS23JaxGJti7Z6Yy/uphHvnEc2/eU89tJn/HNJz6mrKIq1qGJNFis6tvzgIuBybULzewowpNlDgLOAf5gZiEzCwEPAecCRwFXBPsC3Af81t2LgG3A9S1zCSLN65yjuzLlzjP5y/UjWL5pN8f951tc9sgUXp2zltba4iCJIyZ9Lu6+EIj09r6xwHPuvg9YYWZLgRHBtqXuvjw47jlgrJktBM4Argz2eRK4G3i4WS9ApAWNLArPujxj5VY+XLqZ7z47i++9MBszI2TGGQM78csLB5PVJllvxJS4EW8d+vnA1FrrJUEZwOoDyo8HOgLb3b0ywv4HMbNxwDiAgoKCKIUs0vwuOLYbFxzbjapq5+8zS1i+eTfV7uzcW8nzn6zin3PXkRIyMlKTGZyfzY2n9ubE3h1J1mAAiZFmSy5mNgnoEmHTXe7+Sl2HRShzIjff+SH2j8jdxwPjIdyhX9d+IvEqlGRcWtzjS2X/dlw+n6zcxs6yCkrLKnhj3gaueuxjijq148dfGUhGSggzo09eWzq2S4tR5NLaNFtycffRjTisBKj9L6c7UDNfeaTyzUB7M0sOai+19xdpFYoLcyguzNm//pOvHMVbCzZw94T5XPvnT/aXd8hI4fVbRtElOz0WYUorE2/NYhOAZ8zsf4FuQBHh6f0NKDKzXsAawp3+V7q7m9m7wCXAc8A1QF21IpFWIT0lxFeP7cYpfXNZuG4nDpSWVfK9F2Zz/oMf0D4jlcKObfnF2EHkt28T63DlCBWT5GJmFwEPEn7p2D/NbLa7j3H3+Wb2ArAAqARucveq4JibgYlACHjc3ecHp/sR8JyZ/RcwC3ishS9HJC51aJv6palk2qQO44Xpq3F33l+8iVPue4eCnAx65bblrKM6c/ZRXcjLVLOZRIceohRphVZt2cPLs9awZGMp89bsYOWWPZjB8J45nNw3l+LCDpysOc4kgqhNuS8iR56CjhncMroIAHdn0fpSXp+3nkkLNvDbSZ8BMHZIN3rltqVTZjpfG96DUJKGOUvDKbmItHJmxsCuWQzsmsX3zurH7n2VPPD2Eh79cAVV1eGWjY2lZdw6ul+MI5VEouQiIl/SNi2ZO88byB3nDsAdbn9xDr97ewmdMtMZM6izhjNLg+gJKxGJyMxISjJ+edFgTurTkR+//CnD75nE09M+p7KqOtbhSZxTh76I1Kusooq3F27k+emrmfzZJtJTkhhZlEd++zZcMaKA/l0y6z+JHBEa2qGv5CIiDVZeWc3r89bxycqtfLBkMxt2lrGvspobR/Xh+F45ZLVJZmiPDiSp8/+IpeRSDyUXkabbvqec//rnQl6cUbK/7BsnFPCfY4/WJJpHKCWXeii5iETP8k272LG3gglz1vLnj1bSO7ctvXLbcttZ/Tg6PzvW4UkU6TkXEWkxvfPaATCkR3sGdcvmldlrmL16O1c//jHnDe5Cm5QQJ/XJpTC3LT06tNFsza2Aai4i0ixWbN7Nzc/MZP2OMkr3VVJeGR5hlpmeTF67NMzggmPzuXxEDzplpqkZLUGoWaweSi4iLae8spqPlm1mc+k+Zq7azq59lWzdvY+Plm4BYECXTG4dXcTZR3XRYIA4p+RSDyUXkdibunwLC9bu5K9TP2f55t2MKMzh8hE9OLlvLp2z9GqAeKTkUg8lF5H4UVlVzYszSrjnnwsp3VfJaf3zeOLaEfUTNNbGAAAQNUlEQVQfKC2uoclFvWoiEnPJoSQuH1HAlB+fyfWn9OL9zzaxZvveWIclTaDkIiJxo11aMt88qRCAqx6bxi9enc/i9aW01haWRKahyCISV3rkZPDd0/sydcVW/jr1c/780UoAhhd24MfnDWRIj/YaWZYA1OciInFrY2kZkxZsZNXWPbwwfTVbd5eTmpzE8MIO3HvxMfTIyYh1iK1OXHfom9mlwN3AQGCEu0+vte1O4HqgCvh3d58YlK8ESoPyypqLM7Mc4HmgEFgJXObu2+qLQclFJLHsLKvg/+asY8nGUp7/ZDV7yqsYXtiBy4p7UJCTwfDCHA1jbgHxnlwGAtXAH4Hba5KLmR0FPAuMALoBk4B+7l4VJJdid998wLl+BWx193vN7A6gg7v/qL4YlFxEEtfqrXuYMGctT/5rJRtL9+0vNwvPEvDSt09SomkmcT39i7svBCK1m44FnnP3fcAKM1tKONFMOcTpxgKnBctPAu8B9SYXEUlcPXIyuOn0vlx/Si/W7Shj/todfLa+lLU7ynhxRgmTFm7g7EFdYh1mqxZvHfr5wNRa6yVBGYADb5qZA3909/FBeWd3Xwfg7uvMrFNdJzezccA4gIKCgmjHLiItLD0lRK9gkkyOCT8v8/GKrfzgxbn8dtISBnXL4r8vHkyK5jJrcc2WXMxsEhDpvw53ufsrdR0Woaym3e5kd18bJI+3zGyRu08+nJiChDQews1ih3OsiMS/5FAS/3Xh0Tz78Sr2lFfx4owSUkJJ3HZWEZ0y9cR/S2q25OLuoxtxWAnQo9Z6d2BtcL6a7xvN7GXCzWWTgQ1m1jWotXQFNjYtchFJZKP65TGqXx4Ad0+YzxP/Wsmb89fz3g9OIzM9JcbRtR7xVlecAFxuZmlm1gsoAj42s7ZmlglgZm2Bs4F5tY65Jli+BqirViQirczPv3oUf752OFt2l/PyrDWxDqdViUmfi5ldBDwI5AH/NLPZ7j7G3eeb2QvAAqASuCkYKdYZeDkYAJAMPOPubwSnuxd4wcyuB1YBl7b09YhIfDIzTu/fiWO7Z3P/pCW8u2gjeZlpnDu4K+3SkjGgQ9tU+gTvo5Ho0UOUInLE+2jpZn739hLKKqpYsWk3pfsq929LMnj9llH075IZwwgTR1wPRRYRaUkn983l5L65AOzeV8mcku1UV0NFVTU3PTOTh95dyq8uOYb0lFCMIz1yKLmISKvSNi2Zk/rk7l+/YkQBj324gglz1pLbLo3cdqn8+tJjOTo/O4ZRJj4lFxFp1W4ZXURBTgY79lawdvteJn+2icvHT+Wy4h587+x+tEvTn8nG0E9NRFq1rPQUrgmm+QdYs30v9/xzAU9OWcncku08eOVQuma3iVl8iSrehiKLiMRUfvs2/OHrw3jwiqHMLdnB6b9+j9+8uZhdtQYBSP1UcxERieC8wV0ZnJ/N/0xczIPvLOXpaasYUZjDGQM6cfFx+SRrSplDUnIREalDj5wMHrhiKNed0os/fbCc2au288b89azauofbx/SPdXhxTclFRKQeQ3q056Erj8Pd+f7f5vDw+8s4rX8exYU5sQ4tbqleJyLSQGbGz88fRI8Obbj+yen8+OVPWbVlT6zDiktKLiIihyE7I4W/XH88/Ttn8vLMNXzjsWk8/N4ySrYpydSm5CIicph65GTwwrdP5Jkbjqe0rIL73ljEmN9O5t3FmpS9huYWExFpgupqZ832vXz7rzNYsXk3XxnclbMHdeGkPh3JSA1FeuNuQmvo3GJKLiIiUbBxZxk3PTOTpRt3sW1PBRCeFLN7hwzOPqoz40b1plNW4r+wTMmlHkouItIcKqqqmTh/PSXb9lJaVsHi9bt4d/FGMlJCDO+Vw1Un9OT0AXW+jT3uaVZkEZEYSAklcf4x3b5UtmLzbn71xiJmr97Od56ewQ/HDKBtWohR/fKO2KllVHMREWkhm0r3cfHDH7F6614AumSl88AVQ+mUmUb3Dm0S4qn/uG4WM7NLgbuBgcAId58elHcEXgSGA0+4+821jhkGPAG0AV4DbnF3N7Mc4HmgEFgJXObu2+qLQclFRGKhvLKa7XvKWbV1D9f++ZP9Ly7rk9eWm07vS3HPHAo6ZsQ4yro1NLnEKk3OAy4GJh9QXgb8FLg9wjEPA+OAouDrnKD8DuBtdy8C3g7WRUTiUmpyEp2y0ikuzOHN743i0auLueeio9mxt5LvvTCHqx+fRkVVdazDbLKY9Lm4+0LgoCF67r4b+NDM+tYuN7OuQJa7TwnWnwIuBF4HxgKnBbs+CbwH/Kj5ohcRiY6u2W3297lcNDSff85dxw9enMu9ry9iSI/2FBd2SNg+mUTp0M8HSmqtlwRlAJ3dfR2Au68zszqHYZjZOMK1HwoKCpopVBGRw5eRmswlw7rz8qw1PPbhCgDyMtOYdNupZGekxDi6w9dszWJmNsnM5kX4GtuY00UoO+zOIncf7+7F7l6cl5fXiDBERJqPmfHUdSOY/IPTeeLa4WzdXc49ry2IdViN0mw1F3cfHcXTlQDda613B9YGyxvMrGtQa+kKaP4FEUlYyaEkCjpmUNAxgxtG9uaR95cxsiiPo/OzSU4y8jLTSE8JxTrMeiVEs1iQOErN7ARgGnA18GCweQJwDXBv8P2V2EQpIhJdt44uYuL89Xz32Vn7y3rntuXV755C27T4/vMdq6HIFxFODnnAdmC2u48Jtq0EsoDUYNvZ7r7AzIr5Yijy68B3g6HIHYEXgAJgFXCpu2+tLwYNRRaRRLB51z7+tWwLVdXVbNlVzn/9cyFnHdWZ43vl0L9LJiOLWraJP66fc4kHSi4ikojue2MRD7+3bP/6ZcXd+e4ZRWSkhmiXnkxacvM2mSm51EPJRUQSVWVVNbvLq3jk/WWMn7ycqurw3/EuWen837+fQm67tGb7bCWXeii5iMiRYNmmXfxr6WbKKqq5741FnDmwEzeM7N1sr2DWxJUiIq1An7x29MlrB8COvRX8/t2lTJy/gUuGdWfl5t10aJvKz84/ih45LTuljGouIiJHkFVb9vDzCfN4d/Emju2ezfLNuymrqCK7TQrpKSHapIR49JpienZs26jzq+YiItIKFXTM4I9XFbNq6276dspk5ebdPPvxKkr3VVJWXsXeiiratMBzMkouIiJHmNTkJPp2ygSgMLctd543sMVjiP+XB4iISMJRchERkahTchERkahTchERkahTchERkahTchERkahTchERkahTchERkahrtdO/mNkm4PNGHp4LbI5iOLGka4lPupb4dKRcS1Ouo6e71/sSmVabXJrCzKY3ZG6dRKBriU+6lvh0pFxLS1yHmsVERCTqlFxERCTqlFwaZ3ysA4giXUt80rXEpyPlWpr9OtTnIiIiUaeai4iIRJ2Si4iIRJ2Sy2Eys3PMbLGZLTWzO2Idz+Ews5Vm9qmZzTaz6UFZjpm9ZWZLgu8dYh1nXczscTPbaGbzapVFjN/CHgju01wzOy52kX9ZHddxt5mtCe7NbDM7r9a2O4PrWGxmY2ITdWRm1sPM3jWzhWY238xuCcoT8b7UdS0Jd2/MLN3MPjazOcG1/CIo72Vm04L78ryZpQblacH60mB7YZODcHd9NfALCAHLgN5AKjAHOCrWcR1G/CuB3APKfgXcESzfAdwX6zgPEf8o4DhgXn3xA+cBrwMGnABMi3X89VzH3cDtEfY9Kvg9SwN6Bb9/oVhfQ634ugLHBcuZwGdBzIl4X+q6loS7N8HPt12wnAJMC37eLwCXB+WPAN8Jlv8f8EiwfDnwfFNjUM3l8IwAlrr7cncvB54DxsY4pqYaCzwZLD8JXBjDWA7J3ScDWw8oriv+scBTHjYVaG9mXVsm0kOr4zrqMhZ4zt33ufsKYCnh38O44O7r3H1msFwKLATyScz7Ute11CVu703w890VrKYEXw6cAbwYlB94X2ru14vAmWZmTYlByeXw5AOra62XcOhfvnjjwJtmNsPMxgVlnd19HYT/cQGdYhZd49QVfyLeq5uDpqLHazVPJsx1BE0pQwn/Lzmh78sB1wIJeG/MLGRms4GNwFuEa1bb3b0y2KV2vPuvJdi+A+jYlM9Xcjk8kTJ5Io3lPtndjwPOBW4ys1GxDqgZJdq9ehjoAwwB1gG/CcoT4jrMrB3wEnCru+881K4RyuLqeiJcS0LeG3evcvchQHfCNaqBkXYLvkf9WpRcDk8J0KPWendgbYxiOWzuvjb4vhF4mfAv3IaaZong+8bYRdgodcWfUPfK3TcEfwyqgT/xRfNK3F+HmaUQ/mP8tLv/PShOyPsS6VoS+d4AuPt24D3CfS7tzSw52FQ73v3XEmzPpuFNtxEpuRyeT4CiYMRFKuGOrwkxjqlBzKytmWXWLANnA/MIx39NsNs1wCuxibDR6op/AnB1MDrpBGBHTTNNPDqg3+EiwvcGwtdxeTCapxdQBHzc0vHVJWiXfwxY6O7/W2tTwt2Xuq4lEe+NmeWZWftguQ0wmnAf0rvAJcFuB96Xmvt1CfCOB737jRbrUQ2J9kV4tMtnhNsv74p1PIcRd2/CI1vmAPNrYifcrvo2sCT4nhPrWA9xDc8SbpaoIPw/revrip9wNf+h4D59ChTHOv56ruMvQZxzg3/oXWvtf1dwHYuBc2Md/wHXcgrh5pO5wOzg67wEvS91XUvC3RvgGGBWEPM84GdBeW/CCXAp8DcgLShPD9aXBtt7NzUGTf8iIiJRp2YxERGJOiUXERGJOiUXERGJOiUXERGJOiUXERGJOiUXkUYws13B90IzuzLK5/7xAev/iub5RVqCkotI0xQCh5VczCxUzy5fSi7uftJhxiQSc0ouIk1zLzAyeM/HbcFkgf9jZp8EEx3eCGBmpwXvCnmG8AN5mNk/gklE59dMJGpm9wJtgvM9HZTV1JIsOPc8C7+X52u1zv2emb1oZovM7OmaGW3N7F4zWxDE8usW/+lIq5Vc/y4icgh3EH7Xx/kAQZLY4e7DzSwN+MjM3gz2HQEc7eHp2QGuc/etwfQcn5jZS+5+h5nd7OEJBw90MeHJE48FcoNjJgfbhgKDCM8V9RFwspktIDxdyQB395rpQERagmouItF1NuG5s2YTnq69I+E5pwA+rpVYAP7dzOYAUwlPGljEoZ0CPOvhSRQ3AO8Dw2udu8TDkyvOJtxctxMoAx41s4uBPU2+OpEGUnIRiS4DvuvuQ4KvXu5eU3PZvX8ns9MITyZ4orsfS3geqPQGnLsu+2otVwHJHn4vxwjCs/xeCLxxWFci0gRKLiJNU0r4lbg1JgLfCaZux8z6BbNQHygb2Obue8xsAOHp0GtU1Bx/gMnA14J+nTzCr0uucxbe4L0k2e7+GnAr4SY1kRahPheRppkLVAbNW08AvyPcJDUz6FTfRORXR78BfNvM5hKeUXdqrW3jgblmNtPdv16r/GXgRMIzWzvwQ3dfHySnSDKBV8wsnXCt57bGXaLI4dOsyCIiEnVqFhMRkahTchERkahTchERkahTchERkahTchERkahTchERkahTchERkaj7/yZRm300H800AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Generating Initial Population\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "v=int(input(\"enter the number of variables: \"))\n",
    "N=int(input(\"Enter the initial population size: \"))\n",
    "xmin=float(input(\"Enter the minimum value of X: \"))\n",
    "xmax=float(input(\"Enter the maximum value of X: \"))\n",
    "iterations=int(input(\"Enter the max number of iterations: \"))\n",
    "def POP(v,N,xmin,xmax):\n",
    "    return ((np.random.rand(N,v))*(2*xmax))-xmax\n",
    "pop=POP(v,N,xmin,xmax)\n",
    "#fitness\n",
    "def fitness(a):\n",
    "    l=0\n",
    "    for value in a:\n",
    "        o=(-value)*np.sin(np.sqrt(abs(value)))\n",
    "        l+=o\n",
    "    return l\n",
    "def findfitness(pop):\n",
    "    return np.apply_along_axis(fitness,1,pop)\n",
    "bests=np.empty((iterations,v))\n",
    "for iteration in range(0,iterations):\n",
    "        fit=findfitness(pop)\n",
    "        #Tournament Selection\n",
    "        parent=np.empty((N,v))\n",
    "        for s in range(0,N):\n",
    "            r1=np.random.randint(0,N)\n",
    "            r2=r1\n",
    "            while r2==r1:\n",
    "                r2=np.random.randint(0,N)\n",
    "            if fit[r1]<fit[r2]:\n",
    "                parent[s]=pop[r1]\n",
    "            else:\n",
    "                parent[s]=pop[r2]\n",
    "        #crossover\n",
    "        Pc=0.8\n",
    "        Uc=20\n",
    "        child=np.empty((int((Pc*N)),v))\n",
    "        for c in range(0,int(Pc*N),2):\n",
    "            for f in range(0,v):\n",
    "                r=np.random.rand()\n",
    "                if r<=0.5:\n",
    "                    b=(2*r)**(1/(1+Uc))\n",
    "                else:\n",
    "                    b=1/((2*(1-r))**(1/(1+Uc)))\n",
    "                child[c,f]= ((1+b)*parent[c,f]+(1-b)*parent[c+1,f])/2\n",
    "                child[c+1,f] =((1-b)*parent[c,f]+(1+b)*parent[c+1,f])/2\n",
    "        #Mutation\n",
    "        mpop=np.concatenate((pop, child), axis=0)\n",
    "        Pm=0.2\n",
    "        Um=20\n",
    "        for i in range(0,int(Pm*mpop.shape[0])):\n",
    "            r3=np.random.randint(0,mpop.shape[0])\n",
    "            for m in range(0,v):\n",
    "                r4=np.random.rand()\n",
    "                if r4<=0.5:\n",
    "                    d=((2*r4)**(1/(1+Um)))-1\n",
    "                else:\n",
    "                    d=1-((2*(1-r4))**(1/(1+Um)))\n",
    "                mpop[r3,m]+=d\n",
    "        fitness_b=findfitness(mpop)\n",
    "        sortindex=np.argsort(fitness_b,axis=0)\n",
    "        mpop=mpop[sortindex,:].reshape(int(N+(Pc*N)),v)\n",
    "        newgen=mpop[0:N,:]\n",
    "        fitness_a=findfitness(newgen)\n",
    "        bests[iteration]=newgen[0]        \n",
    "        pop=newgen\n",
    "bestcost=findfitness(bests)\n",
    "print(bestcost)\n",
    "plt.plot(bestcost)\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Gbest Cost')\n",
    "plt.title('Real GA')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}