GA_Jupyter.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "import scala.util.Random\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scala.util.Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "data": {
      "text/plain": [
       "null"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  val n = 5;  //number of cities\n",
    "  val A = Array.ofDim[Int](n, n) // adjaceny matrix\n",
    "/*\n",
    "  A(0)(0) = 0\n",
    "A(0)(1) = 5\n",
    "A(0)(2) = 8\n",
    "A(0)(3) = 4\n",
    "A(0)(4) = 5\n",
    "A(0)(5)=7\n",
    "A(0)(6)=6\n",
    "A(0)(7)=4\n",
    "\n",
    "\n",
    "\n",
    "A(1)(0) = 5\n",
    "A(1)(1) = 0\n",
    "A(1)(2) = 7\n",
    "A(1)(3) = 4\n",
    "A(1)(4) = 5\n",
    "A(1)(5)=4\n",
    "A(1)(6)=12\n",
    "A(1)(7)=2\n",
    "\n",
    "\n",
    "\n",
    "A(2)(0) = 8\n",
    "A(2)(1) = 7\n",
    "A(2)(2) = 0\n",
    "A(2)(3) =8\n",
    "A(2)(4) = 6\n",
    "A(2)(5)=20\n",
    "A(2)(6)=8\n",
    "A(2)(7)=3\n",
    "\n",
    "\n",
    "\n",
    "A(3)(0) = 4\n",
    "A(3)(1) = 4\n",
    "A(3)(2) =8\n",
    "A(3)(3) =0\n",
    "A(3)(4) = 8\n",
    "A(3)(5)=2\n",
    "A(3)(6)=1\n",
    "A(3)(7)=3\n",
    "\n",
    "\n",
    "\n",
    "A(4)(0) = 5\n",
    "A(4)(1) = 5\n",
    "A(4)(2) =6\n",
    "A(4)(3) =8\n",
    "A(4)(4) = 0\n",
    "A(4)(5)=23\n",
    "A(4)(6)=12\n",
    "A(4)(7)=14\n",
    "\n",
    "\n",
    "\n",
    "A(5)(0)=15\n",
    "A(5)(1)=19\n",
    "A(5)(2)=25\n",
    "A(5)(3)=37\n",
    "A(5)(4)=28\n",
    "A(5)(5)=0\n",
    "A(5)(6)=3\n",
    "A(5)(7)=5\n",
    "\n",
    "\n",
    "\n",
    "A(6)(0)=12\n",
    "A(6)(1)=4\n",
    "A(6)(2)=12\n",
    "A(6)(3)=21\n",
    "A(6)(4)=32\n",
    "A(6)(5)=21\n",
    "A(6)(6)=0\n",
    "A(6)(7)=28\n",
    "\n",
    "\n",
    "\n",
    "A(7)(0)=6\n",
    "A(7)(1)=7\n",
    "A(7)(2)=8\n",
    "A(7)(3)=4\n",
    "A(7)(4)=21\n",
    "A(7)(5)=12\n",
    "A(7)(6)=14\n",
    "A(7)(7)=0\n",
    " \n",
    "*/\n",
    "\n",
    "A(0)(0) = 0\n",
    "A(0)(1) = 5\n",
    "A(0)(2) = 8\n",
    "A(0)(3) = 4\n",
    "A(0)(4) = 5\n",
    "\n",
    "A(1)(0) = 5\n",
    "A(1)(1) = 0\n",
    "A(1)(2) = 7\n",
    "A(1)(3) = 4\n",
    "A(1)(4) = 5\n",
    "\n",
    "A(2)(0) = 8\n",
    "A(2)(1) = 7\n",
    "A(2)(2) = 0\n",
    "A(2)(3) =8\n",
    "A(2)(4) = 6\n",
    "\n",
    "A(3)(0) = 4\n",
    "A(3)(1) = 4\n",
    "A(3)(2) =8\n",
    "A(3)(3) =0\n",
    "A(3)(4) = 8\n",
    "\n",
    "A(4)(0) = 5\n",
    "A(4)(1) = 5\n",
    "A(4)(2) =6\n",
    "A(4)(3) =8\n",
    "A(4)(4) = 0\n",
    "\n",
    "\n",
    "  var bestFit = 9999.0\n",
    "  \n",
    "  def fitness(city:List[Int]):Float = {\n",
    "    \n",
    "    def dist:Float = {\n",
    "    var dist = 0\n",
    "    for (i <-0 to city.length-2){\n",
    "       // println(A(city(i)-1)(city(i+1)-1))\n",
    "       dist = dist + A(city(i)-1)(city(i+1)-1)\n",
    "    }\n",
    "    //println(A(city(city.length-1)-1)(city(0)-1))\n",
    "    dist = dist +A(city(city.length-1)-1)(city(0)-1)\n",
    "        dist\n",
    "    }\n",
    "    var fitness = dist\n",
    "    if(fitness < bestFit){\n",
    "    bestFit = fitness\n",
    "    }\n",
    "    fitness\n",
    "  }\n",
    "\n",
    "  //Step - 0 - Representation\n",
    "  val cities = (1 to n)\n",
    "  \n",
    "  //Step 1 - Generating the first random population\n",
    "    var p = (0 to 100).map{ i => val city:List[Int] = \n",
    "                           (Random.shuffle(cities)).toList\n",
    "                                (city,fitness(city)) }\n",
    "  \n",
    "   var avgFit = p.map(_._2).reduce(_ + _) / p.length.toFloat\n",
    "  \n",
    "\n",
    "  //Step 2 - Fitness\n",
    "  \n",
    "  \n",
    "   def rt = scala.util.Random.nextInt(n)\n",
    "   def die = scala.util.Random.nextInt(4)\n",
    "   \n",
    "   def breed(p1: List[Int], p2:  List[Int]) = {\n",
    "    val c1Point = rt\n",
    "\n",
    "    // Crossover...\n",
    "    var child1 = p1.splitAt(c1Point)._1 ++ p2.filter(h => !(p1.splitAt(c1Point)._1.contains(h)))\n",
    "    var child2 = p2.splitAt(c1Point)._1 ++ p1.filter(h => !(p2.splitAt(c1Point)._1.contains(h)))\n",
    "\n",
    "    // Mutation... probabilistic\n",
    "    val c2Point = rt\n",
    "    die match {\n",
    "      case 1 => (child1.updated(c1Point,child1(c2Point)).updated(c2Point,child1(c1Point)), child2)\n",
    "      case 2 => (child1,child2.updated(c1Point,child2(c2Point)).updated(c2Point,child2(c1Point)) )\n",
    "      case _ => (child1, child2)\n",
    "    } \n",
    "  }\n",
    "\n",
    "\n",
    "  \n",
    "  \n",
    "  def select = {\n",
    "\n",
    "    // Selection\n",
    "      val pGood = p.sortBy(_._2).take(25) \n",
    "      //val pGood = p.sorted(Ordering.DataType.reverse) \n",
    "      \n",
    "\n",
    "    def rg = scala.util.Random.nextInt(pGood.length)\n",
    "\n",
    "    // New p\n",
    "    p = (0 to 500).flatMap { j =>\n",
    "      val p1 = pGood(rg)\n",
    "      val p2 = pGood(rg)\n",
    "      if( p1._2 > bestFit || p2._2 > bestFit){\n",
    "      val (c1, c2) = breed(p1._1, p2._1)\n",
    "      List((c1,fitness(c1)), (c2,fitness(c2)))\n",
    "      }\n",
    "      else{\n",
    "      val (c1, c2) = breed(pGood(rg)._1, pGood(rg)._1)\n",
    "      List((c1,fitness(c1)), (c2,fitness(c2)))\n",
    "      }\n",
    "    }\n",
    "\n",
    "    avgFit = p.map(_._2).reduce(_ + _) / p.length.toFloat\n",
    "    avgFit\n",
    "\n",
    "  }\n",
    "  \n",
    "  var i = 100;\n",
    "\n",
    "  while (i > 0) {\n",
    "    i -= 1\n",
    "    println(i + \" \" + select )\n",
    "  }\n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(List(5, 3, 2, 4, 1),26.0)]]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.filter(t => t._2 == bestFit).distinct\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1002"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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(List(2, 6, 7, 8, 5, 3, 1, 4),22.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 4, 7, 1, 5),10.0), (List(2, 7, 3, 6, 5, 8, 1, 4),8.0), (List(2, 6, 3, 7, 8, 5, 1, 4),13.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(8, 6, 3, 2, 5, 7, 1, 4),16.0), (List(2, 6, 3, 7, 5, 8, 1, 4),8.0), (List(2, 6, 3, 8, 5, 1, 7, 4),9.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 7, 3, 8, 5, 6, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 6, 3, 8, 5, 7, 1, 4),8.0), (List(2, 4, 3, 7, 5, 8, 1, 6),12.0)]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(List(2, 3, 5, 1, 4),26.0), (List(4, 2, 3, 5, 1),26.0), (List(4, 1, 5, 3, 2),26.0), (List(3, 2, 4, 1, 5),26.0), (List(5, 3, 2, 4, 1),26.0), (List(5, 2, 4, 1, 3),27.0), (List(2, 5, 3, 1, 4),27.0), (List(1, 4, 2, 5, 3),27.0), (List(5, 2, 4, 1, 3),27.0), (List(2, 4, 1, 3, 5),27.0)]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val pGood = p.sortBy(_._2).take(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 2, 4, 1, 5]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val p1 = List(1,3,4,2,5)\n",
    "val p2 = List(3,2,4,1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 3, 2, 4, 5]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val c1Point = 2\n",
    "p1.splitAt(c1Point)._1 ++ p2.filter(h => !(p1.splitAt(c1Point)._1.contains(h)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(List(1, 3),List(4, 2, 5))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1.splitAt(c1Point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 4, 5]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " p2.filter(h => !(p1.splitAt(c1Point)._1.contains(h)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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