Commit 35d1c832 by Febby B. Simanjuntak

Merge branch 'EA' into 'master'

join EA See merge request !1
parents 9b11ff34 c260a03f
{
"cells": [
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"cell_type": "markdown",
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"EA"
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
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"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": []
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