examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea7c9a9b-4398-4dc6-88ca-13eed1528e06",
   "metadata": {},
   "source": [
    "# ModifiedGA\n",
    "\n",
    "---\n",
    "\n",
    "```python\n",
    "mGA = modifiedGA.algorithm( popSize, nGen, nVar, mins, maxs, problem, optimType, info )\n",
    "results, values = mGA.optimize()\n",
    "mGA.saveResults( name, graph, xlabel, ylabel, title )\n",
    "```\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfc9ef87-725a-4250-9759-765e88eea21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import modifiedGA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa698d68-b9e8-4247-a076-640ef4c54e25",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Example 1: Minimization\n",
    "\n",
    "<br>\n",
    "\n",
    "$f(x)=(x+5)^2 +5x$\n",
    "\n",
    "<br>\n",
    "\n",
    "[WolframAlpha result](https://www.wolframalpha.com/input?i=minimize+%28x%2B5%29**2%2B5*x)\n",
    "\n",
    "Minimum $f(x)$ is $-31.25$ where $x=-7.5$\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e6645e38-38a9-483f-b66d-96b2126bc71a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Generation:  Result=-31.249996676093595,  Values=[-7.501823158360306]\n",
      "2. Generation:  Result=-31.249996676093595,  Values=[-7.501823158360306]\n",
      "3. Generation:  Result=-31.249998268162084,  Values=[-7.498684006870432]\n",
      "4. Generation:  Result=-31.249999935695858,  Values=[-7.500253582615369]\n",
      "5. Generation:  Result=-31.249999935695858,  Values=[-7.500253582615369]\n",
      "6. Generation:  Result=-31.249999935695858,  Values=[-7.500253582615369]\n",
      "7. Generation:  Result=-31.24999996228217,  Values=[-7.500194210791715]\n",
      "8. Generation:  Result=-31.249999998482718,  Values=[-7.499961047751061]\n",
      "9. Generation:  Result=-31.249999998482718,  Values=[-7.499961047751061]\n",
      "10. Generation:  Result=-31.249999999730704,  Values=[-7.500016410095558]\n",
      "\n",
      "Result = -31.249999999730704\n",
      "Values = [-7.500016410095558]\n",
      "\n",
      "Calculation time: 00:00:00.01\n",
      "\n",
      "Minimum f(x) is -31.25 where x = -7.5\n"
     ]
    }
   ],
   "source": [
    "def problem(x):\n",
    "    return (x[0]+5)**2 +5*x[0]\n",
    "\n",
    "mGA = modifiedGA.algorithm( popSize=100,\n",
    "                            nGen = 10,\n",
    "                            nVar = 1,\n",
    "                            mins = -10,\n",
    "                            maxs = 10,\n",
    "                            problem = problem,\n",
    "                            info = True )\n",
    "\n",
    "result, variables = mGA.optimize()\n",
    "\n",
    "print(f'Minimum f(x) is {result:.2f} where x = {variables[0]:.1f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb13d4e6-8b66-4c2d-9838-a632903d374d",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Example 2: Maximization\n",
    "\n",
    "<br>\n",
    "\n",
    "$f(x)=sin(x)\\cdot (x-5)+cos(x)\\cdot x\\;,\\qquad -15<x<5$\n",
    "\n",
    "<br>\n",
    "\n",
    "[WolframAlpha result](https://www.wolframalpha.com/input?i=maximize+sin%28x%29*%28x-5%29+%2Bcos%28x%29*x%2C+-15%3Cx%3C5)\n",
    "\n",
    "Maximum $f(x)$ is $24.7307$ where $x=-14.8363$\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5b2330b-21e2-475d-bb49-48c645f20d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum f(x) is 24.7301 where x = -14.8294\n"
     ]
    }
   ],
   "source": [
    "from math import *\n",
    "\n",
    "def problem(x):\n",
    "    return sin(x[0])*(x[0]-5) +cos(x[0])*x[0]\n",
    "\n",
    "mGA = modifiedGA.algorithm( popSize=100,\n",
    "                            nGen = 100,\n",
    "                            nVar = 1,\n",
    "                            mins = [-15],\n",
    "                            maxs = [5],\n",
    "                            problem = problem,\n",
    "                            optimType = 'maximum',\n",
    "                            info = False )\n",
    "\n",
    "result, variables = mGA.optimize()\n",
    "\n",
    "print(f'Minimum f(x) is {result:.4f} where x = {variables[0]:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f5c28a-18e8-4d00-a4d5-6495593e6da7",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Example 3:\n",
    "\n",
    "<br>\n",
    "\n",
    "$f(x)=sin(x)^2sin(y)+cos(x)\\;,\\qquad 0<x<3\\quad and \\quad -3<y<3$\n",
    "\n",
    "<br>\n",
    "\n",
    "[WolframAlpha result](https://www.wolframalpha.com/input?i=minimize+sin%28x%29**2+*+sin%28y%29+%2Bcos%28x%29%2C+0%3Cx%3C3%2C+-3%3Cy%3C3)\n",
    "\n",
    "Minimum $f(x)$ is $-1.25$ where $x=2.0944$ and $y=-1.5708$\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e18f1f4-63d9-4e03-8791-5cbfbcac2973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Generation:  Result=-1.181482607411157,  Values=[2.3825366468319276, -1.8448258426837123]\n",
      "2. Generation:  Result=-1.2439834000281693,  Values=[2.139147376260839, -1.457496857261514]\n",
      "3. Generation:  Result=-1.2491047952727792,  Values=[2.0743734759533847, -1.5315371100643753]\n",
      "4. Generation:  Result=-1.2495688326738916,  Values=[2.106760426107112, -1.6000978222154896]\n",
      "5. Generation:  Result=-1.2499796478151464,  Values=[2.0905669510302483, -1.5658174661399324]\n",
      "6. Generation:  Result=-1.2499862886832864,  Values=[2.09866368856868, -1.571257718337335]\n",
      "7. Generation:  Result=-1.2499951852150524,  Values=[2.0955285259645238, -1.5675892581493909]\n",
      "8. Generation:  Result=-1.249999958658959,  Values=[2.0946153197994644, -1.5706811440233817]\n",
      "9. Generation:  Result=-1.2499999865351128,  Values=[2.094288715657921, -1.5706811440233817]\n",
      "10. Generation:  Result=-1.2499999913655186,  Values=[2.0942962230713036, -1.57085522951301]\n",
      "11. Generation:  Result=-1.2499999986493897,  Values=[2.0944172212223284, -1.5707451093997127]\n",
      "12. Generation:  Result=-1.2499999998566556,  Values=[2.0944055843791114, -1.5708090747760437]\n",
      "13. Generation:  Result=-1.249999999968764,  Values=[2.0943956150473233, -1.57078722901524]\n",
      "14. Generation:  Result=-1.2499999999973632,  Values=[2.094394823116901, -1.5707989488751366]\n",
      "15. Generation:  Result=-1.2499999999997278,  Values=[2.0943956150473233, -1.5707967737560442]\n",
      "16. Generation:  Result=-1.2499999999999147,  Values=[2.094395219082112, -1.5707967737560442]\n",
      "17. Generation:  Result=-1.2499999999999853,  Values=[2.094395240373076, -1.5707962966372824]\n",
      "18. Generation:  Result=-1.249999999999986,  Values=[2.0943951396570646, -1.5707961409375923]\n",
      "19. Generation:  Result=-1.249999999999988,  Values=[2.094395219082112, -1.570796393776265]\n",
      "20. Generation:  Result=-1.2499999999999982,  Values=[2.094395057317891, -1.5707963380671277]\n",
      "\n",
      "Result = -1.2499999999999982\n",
      "Values = [2.094395057317891, -1.5707963380671277]\n",
      "\n",
      "Calculation time: 00:00:00.01\n",
      "\n",
      "Minimum f(x) is -1.25 where x = 2.0944 and y = -1.5708\n"
     ]
    }
   ],
   "source": [
    "def problem(x):\n",
    "    return sin(x[0])**2 * sin(x[1]) +cos(x[0])\n",
    "\n",
    "mGA = modifiedGA.algorithm( popSize=100,\n",
    "                            nGen = 20,\n",
    "                            nVar = 2,\n",
    "                            mins = [0, -3],\n",
    "                            maxs = [3, 3],\n",
    "                            problem = problem,\n",
    "                            info = True )\n",
    "\n",
    "result, variables = mGA.optimize()\n",
    "\n",
    "print(f'Minimum f(x) is {result:.2f} where x = {variables[0]:.4f} and y = {variables[1]:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182c68f1-aafb-4cdc-b2bb-1ed5affbdd54",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Example 4:\n",
    "\n",
    "<br>\n",
    "\n",
    "$f(x)=(x-5)^2 + (y-3)^2 +(xy)^2 + x^2 y\\;,\\qquad -10<x<10\\quad and \\quad -10<y<10$\n",
    "\n",
    "<br>\n",
    "\n",
    "[WolframAlpha result](https://www.wolframalpha.com/input?i=minimize+%28x-5%29**2+%2B+%28y-3%29**2+%2B%28x*y%29**2+%2B+x**2*y%2C+-10%3Cx%3C10%2C+-10%3Cy%3C10)\n",
    "\n",
    "Minimum $f(x)$ is $3.64497$ where $x=6.61268$ and $y=-0.421748$\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54afdb7d-8ac7-4512-9082-0fc0d05b262f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum f(x) is 3.64497 where x = 6.61268 and y = -0.421748\n"
     ]
    }
   ],
   "source": [
    "def problem(x):\n",
    "    return (x[0]-5)**2 + (x[1]-3)**2 +(x[0]*x[1])**2 + x[0]**2*x[1]\n",
    "\n",
    "mGA = modifiedGA.algorithm( popSize=100,\n",
    "                            nGen = 100,\n",
    "                            nVar = 2,\n",
    "                            mins = [-10, -10],\n",
    "                            maxs = [10, 10],\n",
    "                            problem = problem,\n",
    "                            info=False )\n",
    "\n",
    "result, variables = mGA.optimize()\n",
    "\n",
    "print(f'Minimum f(x) is {result:.5f} where x = {variables[0]:.5f} and y = {variables[1]:.6f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968fb299-58fc-46d5-baee-4205eefe060b",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Example 5:\n",
    "\n",
    "<br>\n",
    "\n",
    "$f(x)=sin(x)\\;sin(y)\\;sin(z)\\;,\\qquad -\\pi<x<-\\pi/2\\;,\\quad -\\pi<y<-\\pi/2 \\quad and \\quad -\\pi<z<-\\pi/2$\n",
    "\n",
    "<br>\n",
    "\n",
    "[WolframAlpha result](https://www.wolframalpha.com/input?i=minimize+sin%28x%29*sin%28y%29*sin%28z%29%2C+-pi%3Cx%3C-pi%2F2%2C+-pi%3Cz%3C-pi%2F2%2C+-pi%3Cz%3C-pi%2F2)\n",
    "\n",
    "Minimum $f(x)$ is $-1$ where $x=-1.5708$, $y=-1.5708$ and $z=-1.5708$\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1343cd4-ae31-4271-9247-730ce544deae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum f(x) is -1 where x = -1.5708, y = -1.5708 and z = -1.5708\n"
     ]
    }
   ],
   "source": [
    "from math import pi\n",
    "\n",
    "def problem(x):\n",
    "    return sin(x[0])*sin(x[1])*sin(x[2])\n",
    "\n",
    "mGA = modifiedGA.algorithm( popSize=100,\n",
    "                            nGen = 100,\n",
    "                            nVar = 3,\n",
    "                            mins = [-pi, -pi, -pi],\n",
    "                            maxs = [-pi/2, -pi/2, -pi/2],\n",
    "                            problem = problem,\n",
    "                            info = False )\n",
    "\n",
    "result, variables = mGA.optimize()\n",
    "\n",
    "print(f'Minimum f(x) is {result:.0f} where x = {variables[0]:.4f}, y = {variables[1]:.4f} and z = {variables[2]:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37909239-4ecb-4d12-94f0-bacdb8545835",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Example 6: Saving results\n",
    "Save the results of the previous calculation\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b5c62d0-8d3a-4ad8-af8f-4b193cfad96a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mGA.saveResults( 'result', True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87069f95-0a71-48ed-948e-90705e99516e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}