bitwise_genetic.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Genetic algorithms\n",
    "\n",
    "These depend on three steps. These are described as mutation, crossover and selection. A basic routine looks like this:\n",
    "\n",
    "1. At the beginning, the initial population *pop* is randomly generated and evaluated (through its fitness)\n",
    "2. Then, the best individuals are identified and ranked.\n",
    "3. The current population is evolved into a new one formed of the *children* of members of the population.\n",
    "4. A small but random number of alleles (genetic descriptors) are mutated .\n",
    "5. Return to step 2 until a convergence criteria is fulfilled."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First part: A function that returns a float from a bit string (for future evaluation of a test function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "#size = 32\n",
    "#bitlist = np.random.randint(2,size=size)\n",
    "#max number\n",
    "#bitlist = np.ones(size, dtype=int)\n",
    "#min number\n",
    "#bitlist = np.zeros(size, dtype=int)\n",
    "\n",
    "#this gives a float between 0 and 1 from the bit list\n",
    "def bit_to_float(bitlist, domain):\n",
    "    res=0\n",
    "    j=-1\n",
    "    for i in range(0,len(bitlist)):\n",
    "        x=2**j\n",
    "        res+=x*bitlist[i]\n",
    "        j-=1\n",
    "    return domain[0] + res*(domain[1]-domain[0])\n",
    "\n",
    "def bit_to_float_array(bit_array, domains):\n",
    "    values = np.zeros(len(bit_array))\n",
    "    if len(domains) == 1:\n",
    "        for i in range(len(bit_array)):\n",
    "            values[i] = bit_to_float(bit_array[i], domains[0])\n",
    "    else:\n",
    "        for i in range(len(bit_array)):\n",
    "            values[i] = bit_to_float(bit_array[i], domains[i])\n",
    "    return values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to optimize\n",
    "#see this: https://en.wikipedia.org/wiki/Test_functions_for_optimization\n",
    "#for some test functions to optimize\n",
    "def ackley(xvec, a=20, b=0.2, c = 2*np.pi):\n",
    "    #domain is -5, 5\n",
    "    d=len(xvec)\n",
    "    func = -a*np.exp(-b*np.sqrt(sum(xvec**2)/d)) - np.exp(sum(np.cos(c*xvec))/d) + a + np.exp(1)\n",
    "    return func\n",
    "\n",
    "def beale(xvec):\n",
    "    #domain is -5, 5\n",
    "    x= xvec[0]\n",
    "    y = xvec[1]\n",
    "    func = (1.5 -x + x*y)**2 + (2.25 - x + x*y**2)**2 + (2.625 - x + x*y**3)**2\n",
    "    return func\n",
    "\n",
    "def evaluate_function(population):\n",
    "    evals = np.zeros(len(population))\n",
    "    for p in range(len(population)):\n",
    "        #evals[p] = ackley(bit_to_float_array(population[p], [[-5,5]]))\n",
    "        evals[p] = beale(bit_to_float_array(population[p], [[-4.5,4.5]]))\n",
    "    return evals\n",
    "\n",
    "def children_from_parents_and_mutate(pair_parents, mutation_rate, breed_method = \"splicing\"):\n",
    "    parent_1 = np.concatenate(pair_parents[0])\n",
    "    parent_2 = np.concatenate(pair_parents[1])\n",
    "    child = parent_1\n",
    "\n",
    "    #how many dimensions:\n",
    "    dim = len(pair_parents[0])\n",
    "    N = len(pair_parents[0][0])\n",
    "\n",
    "    if breed_method == \"dna\":\n",
    "        for j in range(len(child)):\n",
    "            randn = np.random.rand()\n",
    "            if randn < (1-mutation_rate)/2:\n",
    "                child[j] = parent_2[j]\n",
    "            elif (1-mutation_rate) <= randn:\n",
    "                child[j] = np.random.randint(2)\n",
    "    elif breed_method == \"splicing\":\n",
    "        for ds in range(dim):\n",
    "            splice_point = np.random.randint(N)\n",
    "            child[(splice_point+ds*N):(ds*N + N)] = parent_2[(splice_point+ds*N):(ds*N + N)]\n",
    "        for j in range(len(child)):\n",
    "            randn = np.random.rand()\n",
    "            if randn < mutation_rate:\n",
    "                child[j] = (1 - child[j])\n",
    "\n",
    "    export_child = np.array_split(child,dim)\n",
    "    return export_child\n",
    "\n",
    "#create other breeding where a block of genes (from index I to M) is taken from each parent\n",
    "\n",
    "\n",
    "def new_population(population, fidelity, N_elite, mutation_rate):\n",
    "    #fidelity\n",
    "    scaled_fidelity = (np.max(fidelity) - fidelity)/(np.max(fidelity) - np.min(fidelity))\n",
    "    #sort them\n",
    "    sorted_fidelity = np.argsort(scaled_fidelity)[::-1] #this gives you the indices of the lowest fidelity to the highest - we are trying to minimize F\n",
    "    #make scaled fidelity into a probability distribution\n",
    "    prob_fidelity = scaled_fidelity/np.sum(scaled_fidelity)\n",
    "    #choose a member for crossover, based on fitness\n",
    "    #np.random.choice(np.arange(0,len(population),1, dtype=int), p=prob_fidelity)\n",
    "\n",
    "    #number of elites is N_elite\n",
    "    new_population = population\n",
    "    #keep some of the fittest individuals\n",
    "    dim = len(population[0])\n",
    "    new_population[0] = population[sorted_fidelity[0]]\n",
    "    for i in range(1, N_elite):\n",
    "        init_indiv = np.concatenate(population[sorted_fidelity[i]])\n",
    "        for j in range(len(init_indiv)):\n",
    "            randn = np.random.rand()\n",
    "            if randn < mutation_rate:\n",
    "                init_indiv[j] = (1 - init_indiv[j])\n",
    "        new_population[i] = np.array_split(init_indiv,dim)\n",
    "        \n",
    "    #create children and mutations on the other individuals\n",
    "    for i in range(N_elite, len(population)):\n",
    "        #create children\n",
    "        cond = True\n",
    "        while cond:\n",
    "            selected = np.random.choice(N,2,p=prob_fidelity)\n",
    "            if selected[0] != selected[1]: #parents need to be different!\n",
    "                cond = False\n",
    "            else:\n",
    "                cond = True\n",
    "\n",
    "        if scaled_fidelity[selected[0]] < scaled_fidelity[selected[1]]:\n",
    "            pair = [population[selected[0]], population[selected[1]]]\n",
    "        else:\n",
    "            pair = [population[selected[1]], population[selected[0]]]\n",
    "        #breed the pair and introduce some mutation\n",
    "        population[i] = children_from_parents_and_mutate(pair, mutation_rate)\n",
    "\n",
    "    return population\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0, best fidelity = 2.6504041300197967\n",
      "iteration 1, best fidelity = 2.057126236214282\n",
      "iteration 2, best fidelity = 2.057126236214282\n",
      "iteration 3, best fidelity = 2.057126236214282\n",
      "iteration 4, best fidelity = 1.2795997977660774\n",
      "iteration 5, best fidelity = 1.2795997977660774\n",
      "iteration 6, best fidelity = 1.2795997977660774\n",
      "iteration 7, best fidelity = 1.2795997977660774\n",
      "iteration 8, best fidelity = 1.2795997977660774\n",
      "iteration 9, best fidelity = 1.2795997977660774\n",
      "iteration 10, best fidelity = 1.2795997977660774\n",
      "iteration 11, best fidelity = 1.2795997977660774\n",
      "iteration 12, best fidelity = 0.843845427697292\n",
      "iteration 13, best fidelity = 0.843845427697292\n",
      "iteration 14, best fidelity = 0.843845427697292\n",
      "iteration 15, best fidelity = 0.843845427697292\n",
      "iteration 16, best fidelity = 0.843845427697292\n",
      "iteration 17, best fidelity = 0.843845427697292\n",
      "iteration 18, best fidelity = 0.843845427697292\n",
      "iteration 19, best fidelity = 0.843845427697292\n",
      "iteration 20, best fidelity = 0.843845427697292\n",
      "iteration 21, best fidelity = 0.843845427697292\n",
      "iteration 22, best fidelity = 0.843845427697292\n",
      "iteration 23, best fidelity = 0.843845427697292\n",
      "iteration 24, best fidelity = 0.843845427697292\n",
      "iteration 25, best fidelity = 0.843845427697292\n",
      "iteration 26, best fidelity = 0.843845427697292\n",
      "iteration 27, best fidelity = 0.843845427697292\n",
      "iteration 28, best fidelity = 0.843845427697292\n",
      "iteration 29, best fidelity = 0.843845427697292\n",
      "iteration 30, best fidelity = 0.843845427697292\n",
      "iteration 31, best fidelity = 0.843845427697292\n",
      "iteration 32, best fidelity = 0.843845427697292\n",
      "iteration 33, best fidelity = 0.843845427697292\n",
      "iteration 34, best fidelity = 0.843845427697292\n",
      "iteration 35, best fidelity = 0.41244657565150133\n",
      "iteration 36, best fidelity = 0.41244540446896183\n",
      "iteration 37, best fidelity = 0.34318091884237356\n",
      "iteration 38, best fidelity = 0.34318091884237356\n",
      "iteration 39, best fidelity = 0.2966776816655814\n",
      "iteration 40, best fidelity = 0.2966776816655814\n",
      "iteration 41, best fidelity = 0.2966776816655814\n",
      "iteration 42, best fidelity = 0.2966776816655814\n",
      "iteration 43, best fidelity = 0.2966776816655814\n",
      "iteration 44, best fidelity = 0.2966776816655814\n",
      "iteration 45, best fidelity = 0.2966776816655814\n",
      "iteration 46, best fidelity = 0.2966776816655814\n",
      "iteration 47, best fidelity = 0.2966776816655814\n",
      "iteration 48, best fidelity = 0.2966776816655814\n",
      "iteration 49, best fidelity = 0.2966776816655814\n",
      "iteration 50, best fidelity = 0.2966776816655814\n",
      "iteration 51, best fidelity = 0.17690326370507994\n",
      "iteration 52, best fidelity = 0.17690326370507994\n",
      "iteration 53, best fidelity = 0.17690326370507994\n",
      "iteration 54, best fidelity = 0.17690326370507994\n",
      "iteration 55, best fidelity = 0.17690326370507994\n",
      "iteration 56, best fidelity = 0.17690326370507994\n",
      "iteration 57, best fidelity = 0.17690326370507994\n",
      "iteration 58, best fidelity = 0.17690326370507994\n",
      "iteration 59, best fidelity = 0.17690326370507994\n",
      "iteration 60, best fidelity = 0.16268277965990216\n",
      "iteration 61, best fidelity = 0.16267208976247283\n",
      "iteration 62, best fidelity = 0.16267208976247283\n",
      "iteration 63, best fidelity = 0.16267208976247283\n",
      "iteration 64, best fidelity = 0.16267208976247283\n",
      "iteration 65, best fidelity = 0.16267208976247283\n",
      "iteration 66, best fidelity = 0.16267208976247283\n",
      "iteration 67, best fidelity = 0.16267208976247283\n",
      "iteration 68, best fidelity = 0.16267208976247283\n",
      "iteration 69, best fidelity = 0.16267208976247283\n",
      "iteration 70, best fidelity = 0.16267208976247283\n",
      "iteration 71, best fidelity = 0.16267208976247283\n",
      "iteration 72, best fidelity = 0.16267208976247283\n",
      "iteration 73, best fidelity = 0.16267208976247283\n",
      "iteration 74, best fidelity = 0.16267208976247283\n",
      "iteration 75, best fidelity = 0.16267208976247283\n",
      "iteration 76, best fidelity = 0.16267208976247283\n",
      "iteration 77, best fidelity = 0.16267208976247283\n",
      "iteration 78, best fidelity = 0.16267208976247283\n",
      "iteration 79, best fidelity = 0.16267208976247283\n",
      "iteration 80, best fidelity = 0.16267208976247283\n",
      "iteration 81, best fidelity = 0.16267208976247283\n",
      "iteration 82, best fidelity = 0.16267208976247283\n",
      "iteration 83, best fidelity = 0.16267208976247283\n",
      "iteration 84, best fidelity = 0.16267208976247283\n",
      "iteration 85, best fidelity = 0.16267208976247283\n",
      "iteration 86, best fidelity = 0.16267208976247283\n",
      "iteration 87, best fidelity = 0.16267208976247283\n",
      "iteration 88, best fidelity = 0.059687183097379005\n",
      "iteration 89, best fidelity = 0.05853087770758342\n",
      "iteration 90, best fidelity = 0.056708331451947785\n",
      "iteration 91, best fidelity = 0.056708331451947785\n",
      "iteration 92, best fidelity = 0.056708331451947785\n",
      "iteration 93, best fidelity = 0.056708331451947785\n",
      "iteration 94, best fidelity = 0.056708331451947785\n",
      "iteration 95, best fidelity = 0.056708331451947785\n",
      "iteration 96, best fidelity = 0.056708331451947785\n",
      "iteration 97, best fidelity = 0.056708331451947785\n",
      "iteration 98, best fidelity = 0.056708331451947785\n",
      "iteration 99, best fidelity = 0.056708331451947785\n",
      "iteration 100, best fidelity = 0.056708331451947785\n",
      "iteration 101, best fidelity = 0.056708331451947785\n",
      "iteration 102, best fidelity = 0.056708331451947785\n",
      "iteration 103, best fidelity = 0.056708331451947785\n",
      "iteration 104, best fidelity = 0.056708331451947785\n",
      "iteration 105, best fidelity = 0.056708331451947785\n",
      "iteration 106, best fidelity = 0.056708331451947785\n",
      "iteration 107, best fidelity = 0.056708331451947785\n",
      "iteration 108, best fidelity = 0.056708331451947785\n",
      "iteration 109, best fidelity = 0.056708331451947785\n",
      "iteration 110, best fidelity = 0.056708331451947785\n",
      "iteration 111, best fidelity = 0.056708331451947785\n",
      "iteration 112, best fidelity = 0.056708331451947785\n",
      "iteration 113, best fidelity = 0.056708331451947785\n",
      "iteration 114, best fidelity = 0.056708331451947785\n",
      "iteration 115, best fidelity = 0.056708331451947785\n",
      "iteration 116, best fidelity = 0.056708331451947785\n",
      "iteration 117, best fidelity = 0.056708331451947785\n",
      "iteration 118, best fidelity = 0.056708331451947785\n",
      "iteration 119, best fidelity = 0.056708331451947785\n",
      "iteration 120, best fidelity = 0.056708331451947785\n",
      "iteration 121, best fidelity = 0.056708331451947785\n",
      "iteration 122, best fidelity = 0.056708331451947785\n",
      "iteration 123, best fidelity = 0.056708331451947785\n",
      "iteration 124, best fidelity = 0.056708331451947785\n",
      "iteration 125, best fidelity = 0.056708331451947785\n",
      "iteration 126, best fidelity = 0.056708331451947785\n",
      "iteration 127, best fidelity = 0.056708331451947785\n",
      "iteration 128, best fidelity = 0.056708331451947785\n",
      "iteration 129, best fidelity = 0.056708331451947785\n",
      "iteration 130, best fidelity = 0.01946542967231927\n",
      "iteration 131, best fidelity = 0.015544504000198422\n",
      "iteration 132, best fidelity = 0.01515682625365566\n",
      "iteration 133, best fidelity = 0.01515682625365566\n",
      "iteration 134, best fidelity = 0.009183751836944993\n",
      "iteration 135, best fidelity = 0.009183750013785737\n",
      "iteration 136, best fidelity = 0.009183750013785737\n",
      "iteration 137, best fidelity = 0.009183750013785737\n",
      "iteration 138, best fidelity = 0.009183750013785737\n",
      "iteration 139, best fidelity = 0.009183750013785737\n",
      "iteration 140, best fidelity = 0.009183750013785737\n",
      "iteration 141, best fidelity = 0.009183750013785737\n",
      "iteration 142, best fidelity = 0.009183750013785737\n",
      "iteration 143, best fidelity = 0.009183750013785737\n",
      "iteration 144, best fidelity = 0.009183750013785737\n",
      "iteration 145, best fidelity = 0.009183750013785737\n",
      "iteration 146, best fidelity = 0.009183750013785737\n",
      "iteration 147, best fidelity = 0.009183750013785737\n",
      "iteration 148, best fidelity = 0.009183750013785737\n",
      "iteration 149, best fidelity = 0.009183750013785737\n",
      "iteration 150, best fidelity = 0.009183750013785737\n",
      "iteration 151, best fidelity = 0.009183750013785737\n",
      "iteration 152, best fidelity = 0.009183750013785737\n",
      "iteration 153, best fidelity = 0.009183750013785737\n",
      "iteration 154, best fidelity = 0.009183750013785737\n",
      "iteration 155, best fidelity = 0.009183750013785737\n",
      "iteration 156, best fidelity = 0.009183750013785737\n",
      "iteration 157, best fidelity = 0.009183750013785737\n",
      "iteration 158, best fidelity = 0.009183750013785737\n",
      "iteration 159, best fidelity = 0.009183750013785737\n",
      "iteration 160, best fidelity = 0.009183750013785737\n",
      "iteration 161, best fidelity = 0.009183750013785737\n",
      "iteration 162, best fidelity = 0.009183750013785737\n",
      "iteration 163, best fidelity = 0.009183750013785737\n",
      "iteration 164, best fidelity = 0.009183750013785737\n",
      "iteration 165, best fidelity = 0.009183750013785737\n",
      "iteration 166, best fidelity = 0.009183750013785737\n",
      "iteration 167, best fidelity = 0.009183750013785737\n",
      "iteration 168, best fidelity = 0.009183750013785737\n",
      "iteration 169, best fidelity = 0.009183750013785737\n",
      "iteration 170, best fidelity = 0.009183750013785737\n",
      "iteration 171, best fidelity = 0.009183750013785737\n",
      "iteration 172, best fidelity = 0.009183750013785737\n",
      "iteration 173, best fidelity = 0.009183750013785737\n",
      "iteration 174, best fidelity = 0.009183750013785737\n",
      "iteration 175, best fidelity = 0.009183750013785737\n",
      "iteration 176, best fidelity = 0.0033009332608203473\n",
      "iteration 177, best fidelity = 0.0019363863400286177\n",
      "iteration 178, best fidelity = 0.0019363863400286177\n",
      "iteration 179, best fidelity = 0.0019363863400286177\n",
      "iteration 180, best fidelity = 0.0019363863400286177\n",
      "iteration 181, best fidelity = 0.0019363863400286177\n",
      "iteration 182, best fidelity = 0.001119216055217328\n",
      "iteration 183, best fidelity = 0.001119216055217328\n",
      "iteration 184, best fidelity = 0.001119216055217328\n",
      "iteration 185, best fidelity = 0.001119216055217328\n",
      "iteration 186, best fidelity = 0.001119216055217328\n",
      "iteration 187, best fidelity = 0.001119216055217328\n",
      "iteration 188, best fidelity = 0.001119216055217328\n",
      "iteration 189, best fidelity = 0.001119216055217328\n",
      "iteration 190, best fidelity = 0.001119216055217328\n",
      "iteration 191, best fidelity = 0.001119216055217328\n",
      "iteration 192, best fidelity = 0.001119216055217328\n",
      "iteration 193, best fidelity = 0.001119216055217328\n",
      "iteration 194, best fidelity = 0.001119216055217328\n",
      "iteration 195, best fidelity = 0.001119216055217328\n",
      "iteration 196, best fidelity = 0.001119216055217328\n",
      "iteration 197, best fidelity = 0.001119216055217328\n",
      "iteration 198, best fidelity = 0.001119216055217328\n",
      "iteration 199, best fidelity = 0.001119216055217328\n",
      "iteration 200, best fidelity = 0.001119216055217328\n",
      "iteration 201, best fidelity = 0.001119216055217328\n",
      "iteration 202, best fidelity = 0.001119216055217328\n",
      "iteration 203, best fidelity = 0.001119216055217328\n",
      "iteration 204, best fidelity = 0.001119216055217328\n",
      "iteration 205, best fidelity = 0.001119216055217328\n",
      "iteration 206, best fidelity = 0.001119216055217328\n",
      "iteration 207, best fidelity = 0.001119216055217328\n",
      "iteration 208, best fidelity = 0.001119216055217328\n",
      "iteration 209, best fidelity = 0.001119216055217328\n",
      "iteration 210, best fidelity = 0.001119216055217328\n",
      "iteration 211, best fidelity = 0.001119216055217328\n",
      "iteration 212, best fidelity = 0.001119216055217328\n",
      "iteration 213, best fidelity = 0.001119216055217328\n",
      "iteration 214, best fidelity = 0.001119216055217328\n",
      "iteration 215, best fidelity = 0.001119216055217328\n",
      "iteration 216, best fidelity = 0.001119216055217328\n",
      "iteration 217, best fidelity = 0.001119216055217328\n",
      "iteration 218, best fidelity = 0.001119216055217328\n",
      "iteration 219, best fidelity = 0.001119216055217328\n",
      "iteration 220, best fidelity = 0.001119216055217328\n",
      "iteration 221, best fidelity = 0.001119216055217328\n",
      "iteration 222, best fidelity = 0.001119216055217328\n",
      "iteration 223, best fidelity = 0.001119216055217328\n",
      "iteration 224, best fidelity = 0.001119216055217328\n",
      "iteration 225, best fidelity = 0.001119216055217328\n",
      "iteration 226, best fidelity = 0.001119216055217328\n",
      "iteration 227, best fidelity = 0.001119216055217328\n",
      "iteration 228, best fidelity = 0.001119216055217328\n",
      "iteration 229, best fidelity = 0.001119216055217328\n",
      "iteration 230, best fidelity = 0.001119216055217328\n",
      "iteration 231, best fidelity = 0.001119216055217328\n",
      "iteration 232, best fidelity = 0.001119216055217328\n",
      "iteration 233, best fidelity = 0.001119216055217328\n",
      "iteration 234, best fidelity = 0.001119216055217328\n",
      "iteration 235, best fidelity = 0.001119216055217328\n",
      "iteration 236, best fidelity = 0.001119216055217328\n",
      "iteration 237, best fidelity = 0.001119216055217328\n",
      "iteration 238, best fidelity = 0.001119216055217328\n",
      "iteration 239, best fidelity = 0.001119216055217328\n",
      "iteration 240, best fidelity = 0.001119216055217328\n",
      "iteration 241, best fidelity = 0.001119216055217328\n",
      "iteration 242, best fidelity = 0.001119216055217328\n",
      "iteration 243, best fidelity = 0.001119216055217328\n",
      "iteration 244, best fidelity = 0.001119216055217328\n",
      "iteration 245, best fidelity = 0.001119216055217328\n",
      "iteration 246, best fidelity = 0.001119216055217328\n",
      "iteration 247, best fidelity = 0.001119216055217328\n",
      "iteration 248, best fidelity = 0.001119216055217328\n",
      "iteration 249, best fidelity = 0.001119216055217328\n",
      "iteration 250, best fidelity = 0.001119216055217328\n",
      "iteration 251, best fidelity = 0.001119216055217328\n",
      "iteration 252, best fidelity = 0.001119216055217328\n",
      "iteration 253, best fidelity = 0.001119216055217328\n",
      "iteration 254, best fidelity = 0.001119216055217328\n",
      "iteration 255, best fidelity = 0.001119216055217328\n",
      "iteration 256, best fidelity = 0.001119216055217328\n",
      "iteration 257, best fidelity = 0.001119216055217328\n",
      "iteration 258, best fidelity = 0.001119216055217328\n",
      "iteration 259, best fidelity = 0.001119216055217328\n",
      "iteration 260, best fidelity = 0.001119216055217328\n",
      "iteration 261, best fidelity = 0.001119216055217328\n",
      "iteration 262, best fidelity = 0.001119216055217328\n",
      "iteration 263, best fidelity = 0.001119216055217328\n",
      "iteration 264, best fidelity = 0.001119216055217328\n",
      "iteration 265, best fidelity = 0.001119216055217328\n",
      "iteration 266, best fidelity = 0.001119216055217328\n",
      "iteration 267, best fidelity = 0.001119216055217328\n",
      "iteration 268, best fidelity = 0.001119216055217328\n",
      "iteration 269, best fidelity = 0.001119216055217328\n",
      "iteration 270, best fidelity = 0.001119216055217328\n",
      "iteration 271, best fidelity = 0.001119216055217328\n",
      "iteration 272, best fidelity = 0.001119216055217328\n",
      "iteration 273, best fidelity = 0.001119216055217328\n",
      "iteration 274, best fidelity = 0.001119216055217328\n",
      "iteration 275, best fidelity = 0.001119216055217328\n",
      "iteration 276, best fidelity = 0.001119216055217328\n",
      "iteration 277, best fidelity = 0.001119216055217328\n",
      "iteration 278, best fidelity = 0.001119216055217328\n",
      "iteration 279, best fidelity = 0.001119216055217328\n",
      "iteration 280, best fidelity = 0.001119216055217328\n",
      "iteration 281, best fidelity = 0.001119216055217328\n",
      "iteration 282, best fidelity = 0.001119216055217328\n",
      "iteration 283, best fidelity = 0.001119216055217328\n",
      "iteration 284, best fidelity = 0.001119216055217328\n",
      "iteration 285, best fidelity = 0.001119216055217328\n",
      "iteration 286, best fidelity = 0.001119216055217328\n",
      "iteration 287, best fidelity = 0.001119216055217328\n",
      "iteration 288, best fidelity = 0.001119216055217328\n",
      "iteration 289, best fidelity = 0.001119216055217328\n",
      "iteration 290, best fidelity = 0.0009392873609707059\n",
      "iteration 291, best fidelity = 0.0009392873609707059\n",
      "iteration 292, best fidelity = 0.0009392873609707059\n",
      "iteration 293, best fidelity = 0.0009392873609707059\n",
      "iteration 294, best fidelity = 0.0009392873609707059\n",
      "iteration 295, best fidelity = 0.0009392873609707059\n",
      "iteration 296, best fidelity = 0.0009392873609707059\n",
      "iteration 297, best fidelity = 0.0009392873609707059\n",
      "iteration 298, best fidelity = 0.0009392873609707059\n",
      "iteration 299, best fidelity = 0.0009392873609707059\n",
      "iteration 300, best fidelity = 0.0009392873609707059\n",
      "iteration 301, best fidelity = 0.0009392873609707059\n",
      "iteration 302, best fidelity = 0.0009392873609707059\n",
      "iteration 303, best fidelity = 0.0009392873609707059\n",
      "iteration 304, best fidelity = 0.0009392873609707059\n",
      "iteration 305, best fidelity = 0.0009392873609707059\n",
      "iteration 306, best fidelity = 0.0009392873609707059\n",
      "iteration 307, best fidelity = 0.0009392873609707059\n",
      "iteration 308, best fidelity = 0.0009392873609707059\n",
      "iteration 309, best fidelity = 0.0009392873609707059\n",
      "iteration 310, best fidelity = 0.0009392873609707059\n",
      "iteration 311, best fidelity = 0.0009392873609707059\n",
      "iteration 312, best fidelity = 0.0009392873609707059\n",
      "iteration 313, best fidelity = 0.0009392873609707059\n",
      "iteration 314, best fidelity = 0.0009392873609707059\n",
      "iteration 315, best fidelity = 0.0009392873609707059\n",
      "iteration 316, best fidelity = 0.0009392873609707059\n",
      "iteration 317, best fidelity = 0.0009392873609707059\n",
      "iteration 318, best fidelity = 0.0009392873609707059\n",
      "iteration 319, best fidelity = 0.0009392873609707059\n",
      "iteration 320, best fidelity = 0.0009392873609707059\n",
      "iteration 321, best fidelity = 0.0009392873609707059\n",
      "iteration 322, best fidelity = 0.0009392873609707059\n",
      "iteration 323, best fidelity = 0.0009392873609707059\n",
      "iteration 324, best fidelity = 0.0009392873609707059\n",
      "iteration 325, best fidelity = 0.0009392873609707059\n",
      "iteration 326, best fidelity = 0.0009392873609707059\n",
      "iteration 327, best fidelity = 0.0009392873609707059\n",
      "iteration 328, best fidelity = 0.0009392873609707059\n",
      "iteration 329, best fidelity = 0.0009392873609707059\n",
      "iteration 330, best fidelity = 0.0009392873609707059\n",
      "iteration 331, best fidelity = 0.0009392873609707059\n",
      "iteration 332, best fidelity = 0.0009392873609707059\n",
      "iteration 333, best fidelity = 0.0009392873609707059\n",
      "iteration 334, best fidelity = 0.0009392873609707059\n",
      "iteration 335, best fidelity = 0.0009392873609707059\n",
      "iteration 336, best fidelity = 0.0009392873609707059\n",
      "iteration 337, best fidelity = 0.0009392873609707059\n",
      "iteration 338, best fidelity = 0.0009392873609707059\n",
      "iteration 339, best fidelity = 0.0009392873609707059\n",
      "iteration 340, best fidelity = 0.0009392873609707059\n",
      "iteration 341, best fidelity = 0.0009392873609707059\n",
      "iteration 342, best fidelity = 0.0009392873609707059\n",
      "iteration 343, best fidelity = 0.0009392873609707059\n",
      "iteration 344, best fidelity = 0.0009392873609707059\n",
      "iteration 345, best fidelity = 0.0009392873609707059\n",
      "iteration 346, best fidelity = 0.0009392873609707059\n",
      "iteration 347, best fidelity = 0.0009392873609707059\n",
      "iteration 348, best fidelity = 0.0009392873609707059\n",
      "iteration 349, best fidelity = 0.0009392873609707059\n",
      "iteration 350, best fidelity = 0.0009392873609707059\n",
      "iteration 351, best fidelity = 0.0009392873609707059\n",
      "iteration 352, best fidelity = 0.0009392873609707059\n",
      "iteration 353, best fidelity = 0.0009392873609707059\n",
      "iteration 354, best fidelity = 0.0009392873609707059\n",
      "iteration 355, best fidelity = 0.0009392873609707059\n",
      "iteration 356, best fidelity = 0.0009392873609707059\n",
      "iteration 357, best fidelity = 0.0009392873609707059\n",
      "iteration 358, best fidelity = 0.0009392873609707059\n",
      "iteration 359, best fidelity = 0.0009392873609707059\n",
      "iteration 360, best fidelity = 0.0009392873609707059\n",
      "iteration 361, best fidelity = 0.0009392873609707059\n",
      "iteration 362, best fidelity = 0.0009392873609707059\n",
      "iteration 363, best fidelity = 0.0009392873609707059\n",
      "iteration 364, best fidelity = 0.0009392873609707059\n",
      "iteration 365, best fidelity = 0.0009392873609707059\n",
      "iteration 366, best fidelity = 0.0009392873609707059\n",
      "iteration 367, best fidelity = 0.0009392873609707059\n",
      "iteration 368, best fidelity = 0.0009392873609707059\n",
      "iteration 369, best fidelity = 0.0009392873609707059\n",
      "iteration 370, best fidelity = 0.0009392873609707059\n",
      "iteration 371, best fidelity = 0.0009392873609707059\n",
      "iteration 372, best fidelity = 0.0009392873609707059\n",
      "iteration 373, best fidelity = 0.0009392873609707059\n",
      "iteration 374, best fidelity = 0.0009392873609707059\n",
      "iteration 375, best fidelity = 0.0009392873609707059\n",
      "iteration 376, best fidelity = 0.0009392873609707059\n",
      "iteration 377, best fidelity = 0.0009392873609707059\n",
      "iteration 378, best fidelity = 0.0009392873609707059\n",
      "iteration 379, best fidelity = 0.0009392873609707059\n",
      "iteration 380, best fidelity = 0.0009392873609707059\n",
      "iteration 381, best fidelity = 0.0009392873609707059\n",
      "iteration 382, best fidelity = 0.0009392873609707059\n",
      "iteration 383, best fidelity = 0.0009392873609707059\n",
      "iteration 384, best fidelity = 0.0009392873609707059\n",
      "iteration 385, best fidelity = 0.0009392873609707059\n",
      "iteration 386, best fidelity = 0.0009392873609707059\n",
      "iteration 387, best fidelity = 0.0009392873609707059\n",
      "iteration 388, best fidelity = 0.0009392873609707059\n",
      "iteration 389, best fidelity = 0.0009392873609707059\n",
      "iteration 390, best fidelity = 0.0009392873609707059\n",
      "iteration 391, best fidelity = 0.0009392873609707059\n",
      "iteration 392, best fidelity = 0.0009392873609707059\n",
      "iteration 393, best fidelity = 0.0009392873609707059\n",
      "iteration 394, best fidelity = 0.0009392873609707059\n",
      "iteration 395, best fidelity = 0.0009392873609707059\n",
      "iteration 396, best fidelity = 0.0009392873609707059\n",
      "iteration 397, best fidelity = 0.0009392873609707059\n",
      "iteration 398, best fidelity = 0.0009392873609707059\n",
      "iteration 399, best fidelity = 0.0009392873609707059\n",
      "iteration 400, best fidelity = 0.0009392873609707059\n",
      "iteration 401, best fidelity = 0.0009392873609707059\n",
      "iteration 402, best fidelity = 0.0009392873609707059\n",
      "iteration 403, best fidelity = 0.0009392873609707059\n",
      "iteration 404, best fidelity = 0.0009392873609707059\n",
      "iteration 405, best fidelity = 0.0009392873609707059\n",
      "iteration 406, best fidelity = 0.0009392873609707059\n",
      "iteration 407, best fidelity = 0.0009392873609707059\n",
      "iteration 408, best fidelity = 0.0009392873609707059\n",
      "iteration 409, best fidelity = 0.0009392873609707059\n",
      "iteration 410, best fidelity = 0.0009392873609707059\n",
      "iteration 411, best fidelity = 0.0009392873609707059\n",
      "iteration 412, best fidelity = 0.0009392873609707059\n",
      "iteration 413, best fidelity = 0.0009392873609707059\n",
      "iteration 414, best fidelity = 0.0009392873609707059\n",
      "iteration 415, best fidelity = 0.0009392873609707059\n",
      "iteration 416, best fidelity = 0.0009392873609707059\n",
      "iteration 417, best fidelity = 0.0009392873609707059\n",
      "iteration 418, best fidelity = 0.0009392873609707059\n",
      "iteration 419, best fidelity = 0.0009392873609707059\n",
      "iteration 420, best fidelity = 0.0009392873609707059\n",
      "iteration 421, best fidelity = 0.0009392873609707059\n",
      "iteration 422, best fidelity = 0.0009392873609707059\n",
      "iteration 423, best fidelity = 0.0009392873609707059\n",
      "iteration 424, best fidelity = 0.0009392873609707059\n",
      "iteration 425, best fidelity = 0.0009392873609707059\n",
      "iteration 426, best fidelity = 0.0009392873609707059\n",
      "iteration 427, best fidelity = 0.0009392873609707059\n",
      "iteration 428, best fidelity = 0.0009392873609707059\n",
      "iteration 429, best fidelity = 0.0009392873609707059\n",
      "iteration 430, best fidelity = 0.0009392873609707059\n",
      "iteration 431, best fidelity = 0.0009392873609707059\n",
      "iteration 432, best fidelity = 0.0009392873609707059\n",
      "iteration 433, best fidelity = 0.0009392873609707059\n",
      "iteration 434, best fidelity = 0.0009392873609707059\n",
      "iteration 435, best fidelity = 0.0009392873609707059\n",
      "iteration 436, best fidelity = 0.0009392873609707059\n",
      "iteration 437, best fidelity = 0.0009392873609707059\n",
      "iteration 438, best fidelity = 0.0009392873609707059\n",
      "iteration 439, best fidelity = 0.0009392873609707059\n",
      "iteration 440, best fidelity = 0.0009392873609707059\n",
      "iteration 441, best fidelity = 0.0009392873609707059\n",
      "iteration 442, best fidelity = 0.0009392873609707059\n",
      "iteration 443, best fidelity = 0.0009392873609707059\n",
      "iteration 444, best fidelity = 0.0009392873609707059\n",
      "iteration 445, best fidelity = 0.0009392873609707059\n",
      "iteration 446, best fidelity = 0.0009392873609707059\n",
      "iteration 447, best fidelity = 0.0009392873609707059\n",
      "iteration 448, best fidelity = 0.0009392873609707059\n",
      "iteration 449, best fidelity = 0.0009392873609707059\n",
      "iteration 450, best fidelity = 0.0009392873609707059\n",
      "iteration 451, best fidelity = 0.0009392873609707059\n",
      "iteration 452, best fidelity = 0.0009392873609707059\n",
      "iteration 453, best fidelity = 0.0009392873609707059\n",
      "iteration 454, best fidelity = 0.0009392873609707059\n",
      "iteration 455, best fidelity = 0.0009392873609707059\n",
      "iteration 456, best fidelity = 0.0009392873609707059\n",
      "iteration 457, best fidelity = 0.0009392873609707059\n",
      "iteration 458, best fidelity = 0.0009392873609707059\n",
      "iteration 459, best fidelity = 0.0009392873609707059\n",
      "iteration 460, best fidelity = 0.0009392873609707059\n",
      "iteration 461, best fidelity = 0.0009392873609707059\n",
      "iteration 462, best fidelity = 0.0009392873609707059\n",
      "iteration 463, best fidelity = 0.0009392873609707059\n",
      "iteration 464, best fidelity = 0.0009392873609707059\n",
      "iteration 465, best fidelity = 0.0009392873609707059\n",
      "iteration 466, best fidelity = 0.0009392873609707059\n",
      "iteration 467, best fidelity = 0.0009392873609707059\n",
      "iteration 468, best fidelity = 0.0009392873609707059\n",
      "iteration 469, best fidelity = 0.0009392873609707059\n",
      "iteration 470, best fidelity = 0.0009392873609707059\n",
      "iteration 471, best fidelity = 0.0009392873609707059\n",
      "iteration 472, best fidelity = 0.0009392873609707059\n",
      "iteration 473, best fidelity = 0.0009392873609707059\n",
      "iteration 474, best fidelity = 0.0009392873609707059\n",
      "iteration 475, best fidelity = 0.0009392873609707059\n",
      "iteration 476, best fidelity = 0.0009392873609707059\n",
      "iteration 477, best fidelity = 0.0009392873609707059\n",
      "iteration 478, best fidelity = 0.0009392873609707059\n",
      "iteration 479, best fidelity = 0.0009392873609707059\n",
      "iteration 480, best fidelity = 0.0009392873609707059\n",
      "iteration 481, best fidelity = 0.0009392873609707059\n",
      "iteration 482, best fidelity = 0.0009392873609707059\n",
      "iteration 483, best fidelity = 0.0009392873609707059\n",
      "iteration 484, best fidelity = 0.0009392873609707059\n",
      "iteration 485, best fidelity = 0.0009392873609707059\n",
      "iteration 486, best fidelity = 0.0009392873609707059\n",
      "iteration 487, best fidelity = 0.0009392873609707059\n",
      "iteration 488, best fidelity = 0.0009392873609707059\n",
      "iteration 489, best fidelity = 0.0009392873609707059\n",
      "iteration 490, best fidelity = 0.0009392873609707059\n",
      "iteration 491, best fidelity = 0.0009392873609707059\n",
      "iteration 492, best fidelity = 0.0009392873609707059\n",
      "iteration 493, best fidelity = 0.0009392873609707059\n",
      "iteration 494, best fidelity = 0.0009392873609707059\n",
      "iteration 495, best fidelity = 0.0009392873609707059\n",
      "iteration 496, best fidelity = 0.0009392873609707059\n",
      "iteration 497, best fidelity = 0.0009392873609707059\n",
      "iteration 498, best fidelity = 0.0009392873609707059\n",
      "iteration 499, best fidelity = 0.0009392873609707059\n"
     ]
    }
   ],
   "source": [
    "#population of N individuals\n",
    "from numpy import linalg as LA\n",
    "N=20\n",
    "nbit = 32\n",
    "size = (2,nbit) #16 bit precision for each number, 2 dimensional problem\n",
    "population = [np.random.randint(2,size=size) for j in range(N)]\n",
    "fidelity = evaluate_function(population)\n",
    "min_mutation_rate = 0.05  #5 percent allele mutation\n",
    "max_mutation_rate = 0.1\n",
    "N_elite = 4\n",
    "#function is beale\n",
    "#domain:\n",
    "domain = [-4.5,4.5]\n",
    "it_spread = 20\n",
    "\n",
    "iteration = 0\n",
    "maxiter = 500\n",
    "kept_fidelity = np.zeros((maxiter, N))\n",
    "away_from_real_minima = np.zeros((maxiter,N))\n",
    "real_minimum = np.array([3,0.5])\n",
    "keep_mutation_rate = np.zeros(maxiter)\n",
    "keep_var_factor = np.zeros(maxiter)\n",
    "\n",
    "while iteration < maxiter:\n",
    "    updated_mutation_rate = min_mutation_rate\n",
    "    #var_factor = 0\n",
    "    #if iteration > it_spread:\n",
    "    #    var_factor = kept_fidelity[iteration -1][0]/(np.mean([kept_fidelity[iteration -1 - j][0] for j in range(it_spread)]))\n",
    "    #updated_mutation_rate += var_factor*(max_mutation_rate - min_mutation_rate)\n",
    "    #keep_mutation_rate[iteration] = updated_mutation_rate\n",
    "    #keep_var_factor[iteration] = var_factor\n",
    "    population = new_population(population, fidelity, N_elite, updated_mutation_rate)\n",
    "    fidelity = evaluate_function(population)\n",
    "    kept_fidelity[iteration] = np.sort(fidelity)\n",
    "    print(\"iteration {}, best fidelity = {}\".format(iteration,np.min(fidelity)))\n",
    "    for i in range(N):\n",
    "        site = bit_to_float_array(population[i], [domain])\n",
    "        away_from_real_minima[iteration, i] = LA.norm(site - real_minimum)\n",
    "\n",
    "    iteration += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and the best individual is...\n",
      "(x,y,...) = [2.95426784 0.48646942] \n",
      "with a f(X) = 0.00043649580264643365 [real min is 0]\n"
     ]
    }
   ],
   "source": [
    "print('and the best individual is...')\n",
    "best_individual = population[np.argsort(kept_fidelity[-1])[0]]\n",
    "best_vec = bit_to_float_array(best_individual, [domain])\n",
    "print('(x,y,...) = {} '.format(best_vec))\n",
    "print('with a f(X) = {} [real min is {}]'.format(beale(best_vec), 0))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the progress of the fidelity to 0 and the absolute minima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mutation_rate' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[7], line 15\u001b[0m\n\u001b[1;32m     13\u001b[0m plt\u001b[39m.\u001b[39mlegend()\n\u001b[1;32m     14\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m'\u001b[39m\u001b[39mDistance from AbsMin for elite\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> 15\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39mPop = \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, N_bit = \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, mutation = \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, N_elite = \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(N,nbit,mutation_rate, N_elite));\n",
      "\u001b[0;31mNameError\u001b[0m: name 'mutation_rate' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "x = [i for i in range(maxiter)]\n",
    "nshow = 4\n",
    "y = np.zeros((nshow, maxiter))\n",
    "for k in range(nshow):\n",
    "    for j in range(maxiter):\n",
    "        #y[k,j] = away_from_real_minima[j,k]\n",
    "        y[k,j] = kept_fidelity[j,k]\n",
    "    plt.plot(x,y[k,:], label = '{}-th Elite individual'.format(k+1))\n",
    "plt.yscale('log')\n",
    "#plt.ylim([10**(-18),10])\n",
    "plt.xlabel('iterations')\n",
    "plt.legend()\n",
    "plt.ylabel('Distance from AbsMin for elite')\n",
    "plt.title('Pop = {}, N_bit = {}, mutation = {}, N_elite = {}'.format(N,nbit,mutation_rate, N_elite));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(maxiter)]\n",
    "plt.plot(x,keep_var_factor, label = 'variance factor')\n",
    "plt.plot(x,keep_mutation_rate, label = 'mutation rate')\n",
    "plt.legend()\n",
    "#plt.yscale('log')\n",
    "#plt.ylim([10**(-18),10])\n",
    "plt.xlabel('iterations')\n",
    "plt.legend()\n",
    "plt.ylabel('Updated Mutation rate based on past variance')\n",
    "plt.title('Pop = {}, N_bit = {}, mutation = {}, N_elite = {}'.format(N,nbit,mutation_rate, N_elite));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "1f37f27245d596fe55b086ef4d07c8e05adeb0de442bb689b24ab3f7802e6e24"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}