Rerun all examples with new code.

Author: vrettasm

examples/binh_and_korn_multiobjective.ipynb

@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c0d16d4b",
    "metadata": {},
    "source": [
     "# Easom function\n",
@@ -24,16 +23,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e5eb17a",
    "metadata": {},
    "source": [
     "### First we import python libraries and set up the directory of our code."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "26b4eb8a",
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,16 +44,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bedebcd1",
    "metadata": {},
    "source": [
     "### Here we import all our custom GA code."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "05edf811",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +75,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3e282d5",
    "metadata": {},
    "source": [
     "### Define the Easom function, which plays also the role of the 'fitness' function.\n",
@@ -91,8 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "8719472f",
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +121,7 @@
     "    is_valid = check_validity(chromosome)\n",
     "    \n",
     "    # Extract the data values as 'x' and 'y', for parsimony.\n",
-    "    x, y = [gene.value for gene in chromosome.genome]\n",
+    "    x, y = chromosome.values()\n",
     "    \n",
     "    # Calculate the function value.\n",
     "    f_val = -np.cos(x) * np.cos(y) * np.exp(-((x - np.pi)**2 + (y - np.pi)**2))\n",
@@ -147,16 +140,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43bd7841",
    "metadata": {},
    "source": [
     "Here we set the GA parameters, such as number of genes, number of chromosomes, etc."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "037270cd",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +179,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe274ad3",
    "metadata": {},
    "source": [
     "### Optimization process.\n",
@@ -198,8 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "0a1c5d14",
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -209,8 +198,8 @@
      "output_type": "stream",
      "text": [
       "Parallel evolution in progress with 4 islands ...\n",
-      "Final Avg. Fitness = 0.2246.\n",
-      "Elapsed time: 1.955 seconds.\n"
+      "Final Avg. Fitness = 0.2212.\n",
+      "Elapsed time: 1.221 seconds.\n"
      ]
     }
    ],
@@ -220,8 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "3b3a2ad8",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -230,8 +218,8 @@
      "text": [
       "Minimum Found: -1.00000\n",
       "\n",
-      "x0 = 3.14277\n",
-      "x1 = 3.14188\n"
+      "x0 = 3.14072\n",
+      "x1 = 3.14087\n"
      ]
     }
    ],
@@ -246,16 +234,15 @@
     "print(f\"Minimum Found: {optimal_fit:.5f}\\n\")\n",
     "\n",
     "# Display each gene value separately.\n",
-    "for i, xi in enumerate(optimal_solution.genome):\n",
-    "    print(f\"x{i} = {xi.value:.5f}\")\n",
+    "for i, xi in enumerate(optimal_solution.values()):\n",
+    "    print(f\"x{i} = {xi:.5f}\")\n",
     "# _end_for_\n",
     "\n",
     "# True minimum: f(\\pi, \\pi) = -1.0"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9f4a70ee",
    "metadata": {},
    "source": [
     "### End of file"
@@ -264,17 +251,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b329d023-912e-4d42-b4d6-cbdec42a2c1d",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv_tf",
+   "display_name": "SOMap",
    "language": "python",
-   "name": "venv_tf"
+   "name": "somap"
   },
   "language_info": {
    "codemirror_mode": {

examples/easom_in_parallel.ipynb

@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2a871b92",
    "metadata": {},
    "source": [
     "# OneMax function\n",
@@ -25,7 +24,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5314f4cb",
    "metadata": {},
    "source": [
     "### First we import python libraries and set up the directory of our code."
@@ -34,7 +32,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e3e288b8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +45,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0ca56c0",
    "metadata": {},
    "source": [
     "### Here we import all our custom GA code."
@@ -57,7 +53,6 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "73015625",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +73,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd95b659",
    "metadata": {},
    "source": [
     "### Define the OneMax fitness function."
@@ -87,15 +81,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "5a2d2bc4",
    "metadata": {},
    "outputs": [],
    "source": [
     "# OneMax function.\n",
     "def fun_OneMax(individual: Chromosome, f_min: bool = False):\n",
     "    \n",
     "    # Compute the function value.\n",
-    "    f_val = fsum([xi.value for xi in individual])\n",
+    "    f_val = fsum(individual.values())\n",
     "\n",
     "    # Condition for termination.\n",
     "    solution_found = f_val == len(individual)\n",
@@ -110,7 +103,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bdadd046",
    "metadata": {},
    "source": [
     "Here we set the GA parameters, such as number of genes, number of chromosomes, etc."
@@ -119,7 +111,6 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "091152b6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +144,6 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0712daf",
    "metadata": {},
    "source": [
     "### Optimization process.\n",
@@ -164,17 +154,16 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b3502a18",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial Avg. Fitness = 24.9700.\n",
-      "StandardGA finished in 33 iterations.\n",
-      "Final   Avg. Fitness = 46.3100.\n",
-      "Elapsed time: 2.655 seconds.\n"
+      "Initial Avg. Fitness = 25.5900\n",
+      "StandardGA finished in 16 iterations.\n",
+      "Final   Avg. Fitness = 45.0500\n",
+      "Elapsed time: 2.212 seconds.\n"
      ]
     }
    ],
@@ -185,14 +174,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2523fc1f",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimum Found: 50.00000\n",
+      "Optimum Found: 50.000000\n",
       "\n",
       "x1 =   1.000000\n",
       "x2 =   1.000000\n",
@@ -255,19 +243,18 @@
     "optimal_fit, _ = fun_OneMax(optimal_solution)\n",
     "\n",
     "# Display the (final) optimal value.\n",
-    "print(f\"Optimum Found: {optimal_fit:.5f}\\n\")\n",
+    "print(f\"Optimum Found: {optimal_fit:.6f}\\n\")\n",
     "\n",
     "# Display each gene value separately.\n",
-    "for i, xi in enumerate(optimal_solution.genome, start=1):\n",
-    "    print(f\"x{i} = {xi.value:>10.6f}\")\n",
+    "for i, xi in enumerate(optimal_solution.values(), start=1):\n",
+    "    print(f\"x{i} = {xi:>10.6f}\")\n",
     "# _end_for_\n",
     "\n",
     "# True maximum: f(1.0, 1.0, ..., 1.0) = M"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e3193c7f",
    "metadata": {},
    "source": [
     "### End of file"
@@ -276,17 +263,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d7b1f97e",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "venv_tf",
+   "display_name": "SOMap",
    "language": "python",
-   "name": "venv_tf"
+   "name": "somap"
   },
   "language_info": {
    "codemirror_mode": {