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Evolutionary.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N_CITIES = 606  \n",
+    "CROSS_RATE = 0.1\n",
+    "MUTATE_RATE = 0.02\n",
+    "POP_SIZE = 500\n",
+    "N_GENERATIONS = 500"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GA(object):\n",
+    "    def __init__(self, DNA_size, cross_rate, mutation_rate, pop_size, ):\n",
+    "        self.DNA_size = DNA_size\n",
+    "        self.cross_rate = cross_rate\n",
+    "        self.mutate_rate = mutation_rate\n",
+    "        self.pop_size = pop_size\n",
+    "\n",
+    "        self.pop = np.vstack([np.random.permutation(DNA_size) for _ in range(pop_size)])\n",
+    "\n",
+    "    def translateDNA(self, DNA, city_position):     # get cities' coord in order\n",
+    "        line_x = np.empty_like(DNA, dtype=np.float64)\n",
+    "        line_y = np.empty_like(DNA, dtype=np.float64)\n",
+    "        for i, d in enumerate(DNA):\n",
+    "            city_coord = city_position[d]\n",
+    "            line_x[i, :] = city_coord[:, 0]\n",
+    "            line_y[i, :] = city_coord[:, 1]\n",
+    "        return line_x, line_y\n",
+    "\n",
+    "    def get_fitness(self, line_x, line_y):\n",
+    "        total_distance = np.empty((line_x.shape[0],), dtype=np.float64)\n",
+    "        for i, (xs, ys) in enumerate(zip(line_x, line_y)):\n",
+    "            total_distance[i] = np.sum(np.sqrt(np.square(np.diff(xs)) + np.square(np.diff(ys))))\n",
+    "        fitness = np.exp(self.DNA_size * 2 / total_distance)\n",
+    "        return fitness, total_distance\n",
+    "\n",
+    "    def select(self, fitness):\n",
+    "        idx = np.random.choice(np.arange(self.pop_size), size=self.pop_size, replace=True, p=fitness / fitness.sum())\n",
+    "        return self.pop[idx]\n",
+    "\n",
+    "    def crossover(self, parent, pop):\n",
+    "        if np.random.rand() < self.cross_rate:\n",
+    "            i_ = np.random.randint(0, self.pop_size, size=1)                        # select another individual from pop\n",
+    "            cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)   # choose crossover points\n",
+    "            keep_city = parent[~cross_points]                                       # find the city number\n",
+    "            swap_city = pop[i_, np.isin(pop[i_].ravel(), keep_city, invert=True)]\n",
+    "            parent[:] = np.concatenate((keep_city, swap_city))\n",
+    "        return parent\n",
+    "\n",
+    "    def mutate(self, child):\n",
+    "        for point in range(self.DNA_size):\n",
+    "            if np.random.rand() < self.mutate_rate:\n",
+    "                swap_point = np.random.randint(0, self.DNA_size)\n",
+    "                swapA, swapB = child[point], child[swap_point]\n",
+    "                child[point], child[swap_point] = swapB, swapA\n",
+    "        return child\n",
+    "\n",
+    "    def evolve(self, fitness):\n",
+    "        pop = self.select(fitness)\n",
+    "        pop_copy = pop.copy()\n",
+    "        for parent in pop:  # for every parent\n",
+    "            child = self.crossover(parent, pop_copy)\n",
+    "            child = self.mutate(child)\n",
+    "            parent[:] = child\n",
+    "        self.pop = pop\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TravelItinerary(object):\n",
+    "    def __init__(self, n_cities):\n",
+    "        self.city_position = np.random.rand(n_cities, 2)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}