Lab Ass 3.1.5 (Real GA, Q5).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: -5.12\n",
      "Enter the maximum value of X: 5.12\n",
      "Enter the max number of iterations: 300\n",
      "[247.81335448 247.81335448 247.81335448 247.81335448 247.81335448\n",
      " 233.5467587  233.11631302 233.11631302 225.28173146 225.15321366\n",
      " 217.96407369 218.65080531 218.31308184 218.20409969 217.6188458\n",
      " 217.6188458  209.46034803 205.97479023 205.97479023 204.99608939\n",
      " 204.99608939 201.51016132 201.51016132 201.3901328  194.31772604\n",
      " 194.29716007 185.89354982 185.76372963 185.71450758 183.25954696\n",
      " 178.39074092 169.6548121  169.06907781 166.05752316 166.04094518\n",
      " 165.73154414 165.41581049 156.03459571 155.93376105 155.91162593\n",
      " 155.91162593 148.93510774 146.31897133 146.31897133 146.31897133\n",
      " 145.96761951 138.50798162 133.31642119 131.63312861 131.63312861\n",
      " 131.23623187 131.23623187 131.23623187 131.23567937 131.23567937\n",
      " 131.23567937 131.23567937 131.23561949 131.23545723 131.23545563\n",
      " 131.23545563 131.2354334  131.2354334  131.23543338 126.79271042\n",
      " 131.15614611 131.15614611 131.15614611 131.15614402 131.15614395\n",
      " 131.15614388 131.15614388 131.15614388 131.15614388 131.15614388\n",
      " 131.15614388 131.15614388 131.15614388 131.15614388 131.15614388\n",
      " 131.15614388 131.15614388 131.15614388 131.15614388 128.83691119\n",
      " 128.70196037 128.70196037 128.7019343  126.72967856 126.72967856\n",
      " 126.72967856 126.72967856 126.73192849 126.73184234 126.73184234\n",
      " 126.73184234 126.73184234 126.73184045 126.73184045 126.73184035\n",
      " 126.73184035 126.73184035 125.76489056 125.76489056 125.76489056\n",
      " 125.72140046 125.72140046 125.72140046 125.7211865  125.71303704\n",
      " 125.71283479 125.71272449 125.71272296 125.71271225 125.71272152\n",
      " 125.71272132 125.71272129 125.71272129 125.71272129 125.71272129\n",
      " 125.71272129 125.71272129 125.71272129 125.71272129 125.71272129\n",
      " 125.71272129 125.71272129 125.71272129 125.71272129 125.71272129\n",
      " 125.71272129 125.71272129 125.71272129 125.02683966 125.05529443\n",
      " 124.88215086 124.72503655 124.72503655 124.66814854 124.66317222\n",
      " 124.66228942 124.66228942 124.66228942 124.66228942 124.66228444\n",
      " 124.66228444 124.66228444 124.66228444 124.66228444 124.66228444\n",
      " 124.66228444 124.66228444 124.66228444 124.66228444 124.66228444\n",
      " 124.66228444 124.66228444 124.66228444 124.66228444 124.66228444\n",
      " 124.66228444 124.66228444 124.66228444 124.66228444 123.82013191\n",
      " 123.82013191 123.56520139 123.55607134 123.53577867 123.52694046\n",
      " 123.52694046 123.52688304 123.52688304 123.52688304 123.52688304\n",
      " 123.52688301 123.52688301 123.52688301 123.52688301 123.52688301\n",
      " 122.81877952 122.81639708 122.58375203 122.56898707 122.56898707\n",
      " 118.81038811 118.81038811 118.81038811 118.80930429 118.80930429\n",
      " 118.8087099  118.8087099  118.80868094 118.80868094 118.80868064\n",
      " 118.80868064 118.80868064 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 118.80868061 118.80868061 118.80868061 118.80868061 118.80868061\n",
      " 117.13250845 117.12026994 117.12026994 117.11571263 117.11571263\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525\n",
      " 117.11428525 117.11428525 117.11428525 117.11428525 117.11428525]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\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**2)-(10*np.cos(2*np.pi*value))+10)\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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}