diff --git a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb
index 75e0971880c5050eee7fbc617c635ce599d3358e..e36ef34871fdf9595eb4fa15e386ff725fe15f24 100644
--- a/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb
+++ b/synced_files/GA_1_7/Solution/Discharge/Analysis_discharge_solution.ipynb
@@ -606,7 +606,7 @@
},
{
"cell_type": "markdown",
- "id": "8f758bdc",
+ "id": "896efc98",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
diff --git a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb
index 6567f282e650f480dd0cf0bec4be73e6ca1e73c9..3c7d67fb7be581811f935df7b582d1b5f088e11d 100644
--- a/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb
+++ b/synced_files/GA_1_7/Solution/Emissions/Analysis_emissions_solution.ipynb
@@ -159,7 +159,7 @@
},
{
"cell_type": "markdown",
- "id": "60c2772b",
+ "id": "5fe35d99",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -262,7 +262,7 @@
},
{
"cell_type": "markdown",
- "id": "47336511",
+ "id": "595ed5e1",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -374,7 +374,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "8b7bc362",
+ "id": "789f8ffe",
"metadata": {},
"outputs": [],
"source": [
@@ -437,7 +437,7 @@
},
{
"cell_type": "markdown",
- "id": "828202de",
+ "id": "2609f730",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -538,7 +538,7 @@
},
{
"cell_type": "markdown",
- "id": "0abd6107",
+ "id": "addd7d94",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -552,7 +552,7 @@
},
{
"cell_type": "markdown",
- "id": "1d6cd58b",
+ "id": "97ec1d42",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -573,7 +573,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9f6048c7",
+ "id": "26a7f2b5",
"metadata": {},
"outputs": [],
"source": [
@@ -589,7 +589,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "730053de",
+ "id": "671ce3c4",
"metadata": {},
"outputs": [],
"source": [
@@ -602,7 +602,7 @@
},
{
"cell_type": "markdown",
- "id": "c36c1228",
+ "id": "78effa24",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
diff --git a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb
index f70dddea769561aff65666e355c52380df95e6d2..875a352685f16e28b4c6a4657ef0e915fa12bdc7 100644
--- a/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb
+++ b/synced_files/GA_1_7/Solution/Force/Analysis_force_solution.ipynb
@@ -164,7 +164,7 @@
},
{
"cell_type": "markdown",
- "id": "4813e021",
+ "id": "47600754",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -269,7 +269,7 @@
},
{
"cell_type": "markdown",
- "id": "72632d52",
+ "id": "ddb49ba6",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -382,7 +382,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "b678de66",
+ "id": "6de8ff43",
"metadata": {},
"outputs": [],
"source": [
@@ -445,7 +445,7 @@
},
{
"cell_type": "markdown",
- "id": "06341330",
+ "id": "4bed4432",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -546,7 +546,7 @@
},
{
"cell_type": "markdown",
- "id": "f0cd2ba2",
+ "id": "643950ec",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -561,7 +561,7 @@
},
{
"cell_type": "markdown",
- "id": "baa64738",
+ "id": "d56dcbe7",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -582,7 +582,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "4b832077",
+ "id": "60905bdb",
"metadata": {},
"outputs": [],
"source": [
@@ -598,7 +598,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "7a12f07f",
+ "id": "8febae98",
"metadata": {},
"outputs": [],
"source": [
@@ -611,7 +611,7 @@
},
{
"cell_type": "markdown",
- "id": "4ee89c3b",
+ "id": "075b32b4",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; width:95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
diff --git a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb
index 819bb4ac8ef8d11578371a10796b45012a93d3c4..017df977da07a5eebe24792ce29e5c152ae6685f 100644
--- a/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb
+++ b/synced_files/GA_1_7/Student/Discharge/Analysis_discharge.ipynb
@@ -384,7 +384,7 @@
},
{
"cell_type": "markdown",
- "id": "5a36d8e1",
+ "id": "b84744a4",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -405,7 +405,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "e43d8b5d",
+ "id": "4de11b60",
"metadata": {},
"outputs": [],
"source": [
@@ -421,7 +421,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "63e7bdd0",
+ "id": "5abb8939",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb
index 578d2a1fcd0d75f13cb25cbba0237acd810a9d1b..da27448e23bdb24baaaaf2d5204e28336b27f368 100644
--- a/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb
+++ b/synced_files/GA_1_7/Student/Emissions/Analysis_emissions.ipynb
@@ -378,7 +378,7 @@
},
{
"cell_type": "markdown",
- "id": "7da4cee5",
+ "id": "d42e2d08",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -399,7 +399,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "bdfcfda3",
+ "id": "a8947efd",
"metadata": {},
"outputs": [],
"source": [
@@ -415,7 +415,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c382e811",
+ "id": "eccf40b6",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb b/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb
index 208e1d35dac0ce07bdfa6694e1b7455134f52dd2..191318c729d6af2c1039340af173e21dc8676b50 100644
--- a/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb
+++ b/synced_files/GA_1_7/Student/Force/Analysis_force.ipynb
@@ -384,7 +384,7 @@
},
{
"cell_type": "markdown",
- "id": "3f307882",
+ "id": "0c674860",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -405,7 +405,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "3069b81b",
+ "id": "3ac217e3",
"metadata": {},
"outputs": [],
"source": [
@@ -421,7 +421,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "0347865f",
+ "id": "071694a1",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb
index ed961c01dae32916387d76987022d332d9a695c9..3e437d59a39951cdaa6c8a966a92280437df6c07 100644
--- a/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb
+++ b/synced_files/GA_1_7/Unused/Temp/Distribution_Fitting_T.ipynb
@@ -163,7 +163,7 @@
},
{
"cell_type": "markdown",
- "id": "2894cf33",
+ "id": "256d920e",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -263,7 +263,7 @@
},
{
"cell_type": "markdown",
- "id": "cedffbee",
+ "id": "973ce758",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -372,7 +372,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "1e5f9dea",
+ "id": "c11b680c",
"metadata": {},
"outputs": [],
"source": [
@@ -434,7 +434,7 @@
},
{
"cell_type": "markdown",
- "id": "00c255ef",
+ "id": "84699219",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -533,7 +533,7 @@
},
{
"cell_type": "markdown",
- "id": "7c9d5887",
+ "id": "9706c82a",
"metadata": {},
"source": [
"<div style=\"background-color:#FAE99E; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
@@ -546,7 +546,7 @@
},
{
"cell_type": "markdown",
- "id": "22003ddf",
+ "id": "3ab96f3c",
"metadata": {},
"source": [
"If you run the code in the cell below, you will obtain a scatter plot of both variables. Explore the relationship between both variables and answer the following questions:\n",
@@ -567,7 +567,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "233d3478",
+ "id": "f64e0385",
"metadata": {},
"outputs": [],
"source": [
diff --git a/synced_files/GA_2_5/old_Analysis_GA_solution.ipynb b/synced_files/GA_2_5/old_Analysis_GA_solution.ipynb
index d65dc0f6a03e39f7231df375c0ab60078ce5623c..47914811d1d9e3b4c9e3f6e39121df421ab4e605 100644
--- a/synced_files/GA_2_5/old_Analysis_GA_solution.ipynb
+++ b/synced_files/GA_2_5/old_Analysis_GA_solution.ipynb
@@ -25,15 +25,6 @@
"*[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 15 December, 2023.*\n"
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0491cc69"
- },
- "source": [
- "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> during the in-class session some of the confusion was caused by code issues. Comments relevant to the code and notebooks as-used in the Friday in-class session are provided in boxes like this.</p></div>"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
@@ -96,25 +87,7 @@
"from pymoo.operators.sampling.rnd import BinaryRandomSampling\n",
"from pymoo.operators.crossover.hux import HalfUniformCrossover #\n",
"from pymoo.operators.mutation.bitflip import BitflipMutation\n",
- "\n",
- "# not used here but generally useful dependencies\n",
- "#from pymoo.core.problem import Problem\n",
- "#from pymoo.operators.mutation.pm import PolynomialMutation\n",
- "#from pymoo.operators.crossover.pntx import PointCrossover #\n",
- "#from pymoo.operators.crossover.sbx import SBX\n",
- "#from pymoo.operators.crossover.sbx import SimulatedBinaryCrossover\n",
- "#from pymoo.operators.crossover.pntx import Crossover\n",
- "#from pymoo.operators.repair.bounds_repair import BoundsRepair\n",
- "#from pymoo.core.repair import Repair"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0491cc69"
- },
- "source": [
- "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> the intention with the commented lines above was to illustrate possible crossover methods you could use, but unfortunately the pymoo documentation was not clear enough to indicate how they could have been used. The methods <code>SinglePointCrossover</code> and <code>TwoPointCrossover</code> (n<em>not</em> listed above!) could have been easily used, as these methods require no additional keyword arguments and would have worked “out of the box;†they were also illustrated with examples in the online textbook. We apologize for this oversight on our part.</p></div>"
+ "from pymoo.operators.crossover.pntx import PointCrossover # "
]
},
{
@@ -443,15 +416,6 @@
"Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input."
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0491cc69"
- },
- "source": [
- "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> the variables <code>extension_max_no</code> and <code>timelimit</code> were both defined here, but never used. <code>extension_max_no</code> is similar to the variable in notebook A with the same name; here it is superceded below by <code>Budget</code>.</p></div>"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -464,11 +428,6 @@
"source": [
"# define parameters, case study (network) list and the directory where their files are\n",
"extension_factor = 1.5 # capacity after extension (1.5 means add 50%)\n",
- "extension_max_no = 20 # the number of links to add capacity to (simplified way of reprsenting a budget for investing)\n",
- "#it's the same to say that it's exactly this number of that this number is the max, that's because every investment brings travel time benefits \n",
- "#even if just one car circulates.\n",
- "timelimit = 300 # seconds\n",
- "beta = 2 # parameter to use in link travel time function (explained later)\n",
"\n",
"networks = ['SiouxFalls']\n",
"networks_dir = os.getcwd() +'/input/TransportationNetworks'\n",
@@ -487,18 +446,7 @@
"# prep links, nodes, and free flow travel times\n",
"links = list(net_data['capacity'].keys())\n",
"nodes = np.unique([list(edge) for edge in links])\n",
- "fftts = net_data['free_flow']\n",
- "\n",
- "# auxiliary parameters (dict format) to keep the problem linear (capacities as parameters rather than variables)\n",
- "cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}\n",
- "cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}\n",
- "\n",
- "# origins and destinations\n",
- "dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])\n",
- "origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])\n",
- "\n",
- "# demand in node-destination form\n",
- "demand = create_nd_matrix(ods_data, origs, dests, nodes)"
+ "fftts = net_data['free_flow']"
]
},
{
@@ -707,25 +655,7 @@
"id": "0491cc69"
},
"source": [
- "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> population size <code>pop_size</code> was 10 originally. If you change this, you will see different results. This is problem-dependent!</p></div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0491cc69"
- },
- "source": [
- "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). </p></div>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0491cc69"
- },
- "source": [
- "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). The initial value of 76 was trivial, and the solution converged quickly. Numbers between 10 and 40 would have produced <em>much</em> more interesting results (see solution explanation).</p></div>"
+ "<div style=\"background-color:#ffa500; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> population size <code>pop_size</code> is 10 originally. If you change this, you will see different results. This is problem-dependent!</p></div>"
]
},
{
@@ -738,17 +668,18 @@
},
"outputs": [],
"source": [
- "Budget = 76\n",
+ "extension_max_no = 40\n",
"pop_size = 10\n",
"\n",
"# initiate an instance of the problem with max number of selected links as budget constraint\n",
- "problem = NDP(budget=Budget)\n",
+ "problem = NDP(budget=extension_max_no)\n",
"\n",
"# initiate the GA with parameters appropriate for binary variables\n",
"method = GA(pop_size=pop_size,\n",
" sampling=BinaryRandomSampling(),\n",
" mutation=BitflipMutation(),\n",
" crossover=HalfUniformCrossover()\n",
+ " # replace HalfUniformCrossover() by PointCrossover(2) for two-point crossover\n",
" )"
]
},
@@ -763,13 +694,20 @@
"Now we are ready to minimize the NDP problem using the GA method we defined."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div style=\"background-color:#a6ffa6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Instructions:</b> Run the GA for 3 minutes initially to observe how the results converge and to understand the process of obtaining the final solution. Once you have familiarized yourself with the mechanism and the behavior of the algorithm, extend the maximum computation time to 10 minutes. Use the results from this extended run as the foundation for addressing the questions outlined in the report. </p></div>"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
"id": "0491cc69"
},
"source": [
- "<div style=\"background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> termination is set here as a keyword argument (see note above).</p></div>"
+ "<div style=\"background-color:#ffa500; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%\"><p><b>Note:</b> Maximum computation time (termination criteria) is set here as a keyword argument.</p></div>"
]
},
{
@@ -785,7 +723,7 @@
"\n",
"opt_results = minimize(problem,\n",
" method,\n",
- " termination=(\"time\", \"00:05:00\"), #5 minute maximum computation time\n",
+ " termination=(\"time\", \"00:03:00\"), #this is maximum computation time\n",
" seed=7,\n",
" save_history=True,\n",
" verbose=True,\n",
diff --git a/synced_files/GA_2_5/old_Analysis_GA_solution.md b/synced_files/GA_2_5/old_Analysis_GA_solution.md
index 69eaf9f38cf499913d8a3bdd7ebf9e477ef9c57d..c6f84b9a5758d69d1dff39b818c61f8f9b83573c 100644
--- a/synced_files/GA_2_5/old_Analysis_GA_solution.md
+++ b/synced_files/GA_2_5/old_Analysis_GA_solution.md
@@ -31,10 +31,6 @@ jupyter:
<!-- #endregion -->
-<!-- #region id="0491cc69" -->
-<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> during the in-class session some of the confusion was caused by code issues. Comments relevant to the code and notebooks as-used in the Friday in-class session are provided in boxes like this.</p></div>
-<!-- #endregion -->
-
<!-- #region pycharm={"name": "#%% md\n"} -->
## Introduction
@@ -75,22 +71,9 @@ from pymoo.optimize import minimize
from pymoo.operators.sampling.rnd import BinaryRandomSampling
from pymoo.operators.crossover.hux import HalfUniformCrossover #
from pymoo.operators.mutation.bitflip import BitflipMutation
-
-# not used here but generally useful dependencies
-#from pymoo.core.problem import Problem
-#from pymoo.operators.mutation.pm import PolynomialMutation
-#from pymoo.operators.crossover.pntx import PointCrossover #
-#from pymoo.operators.crossover.sbx import SBX
-#from pymoo.operators.crossover.sbx import SimulatedBinaryCrossover
-#from pymoo.operators.crossover.pntx import Crossover
-#from pymoo.operators.repair.bounds_repair import BoundsRepair
-#from pymoo.core.repair import Repair
+from pymoo.operators.crossover.pntx import PointCrossover #
```
-<!-- #region id="0491cc69" -->
-<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> the intention with the commented lines above was to illustrate possible crossover methods you could use, but unfortunately the pymoo documentation was not clear enough to indicate how they could have been used. The methods <code>SinglePointCrossover</code> and <code>TwoPointCrossover</code> (n<em>not</em> listed above!) could have been easily used, as these methods require no additional keyword arguments and would have worked “out of the box;†they were also illustrated with examples in the online textbook. We apologize for this oversight on our part.</p></div>
-<!-- #endregion -->
-
```python
# For visualization
from utils.network_visualization import network_visualization
@@ -341,18 +324,9 @@ def create_nd_matrix(ods_data, origins, destinations, nodes):
Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input.
<!-- #endregion -->
-<!-- #region id="0491cc69" -->
-<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> the variables <code>extension_max_no</code> and <code>timelimit</code> were both defined here, but never used. <code>extension_max_no</code> is similar to the variable in notebook A with the same name; here it is superceded below by <code>Budget</code>.</p></div>
-<!-- #endregion -->
-
```python pycharm={"name": "#%%\n"}
# define parameters, case study (network) list and the directory where their files are
extension_factor = 1.5 # capacity after extension (1.5 means add 50%)
-extension_max_no = 20 # the number of links to add capacity to (simplified way of reprsenting a budget for investing)
-#it's the same to say that it's exactly this number of that this number is the max, that's because every investment brings travel time benefits
-#even if just one car circulates.
-timelimit = 300 # seconds
-beta = 2 # parameter to use in link travel time function (explained later)
networks = ['SiouxFalls']
networks_dir = os.getcwd() +'/input/TransportationNetworks'
@@ -372,17 +346,6 @@ net_data, ods_data = net_dict[networks[0]], ods_dict[networks[0]]
links = list(net_data['capacity'].keys())
nodes = np.unique([list(edge) for edge in links])
fftts = net_data['free_flow']
-
-# auxiliary parameters (dict format) to keep the problem linear (capacities as parameters rather than variables)
-cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}
-cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}
-
-# origins and destinations
-dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])
-origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])
-
-# demand in node-destination form
-demand = create_nd_matrix(ods_data, origs, dests, nodes)
```
## Network Display
@@ -518,29 +481,22 @@ Now, let's initiate an instance of the problem based on the problem class we def
<!-- #endregion -->
<!-- #region id="0491cc69" -->
-<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> population size <code>pop_size</code> was 10 originally. If you change this, you will see different results. This is problem-dependent!</p></div>
-<!-- #endregion -->
-
-<!-- #region id="0491cc69" -->
-<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). </p></div>
-<!-- #endregion -->
-
-<!-- #region id="0491cc69" -->
-<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). The initial value of 76 was trivial, and the solution converged quickly. Numbers between 10 and 40 would have produced <em>much</em> more interesting results (see solution explanation).</p></div>
+<div style="background-color:#ffa500; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> population size <code>pop_size</code> is 10 originally. If you change this, you will see different results. This is problem-dependent!</p></div>
<!-- #endregion -->
```python pycharm={"name": "#%%\n"}
-Budget = 76
+extension_max_no = 40
pop_size = 10
# initiate an instance of the problem with max number of selected links as budget constraint
-problem = NDP(budget=Budget)
+problem = NDP(budget=extension_max_no)
# initiate the GA with parameters appropriate for binary variables
method = GA(pop_size=pop_size,
sampling=BinaryRandomSampling(),
mutation=BitflipMutation(),
crossover=HalfUniformCrossover()
+ # replace HalfUniformCrossover() by PointCrossover(2) for two-point crossover
)
```
@@ -548,15 +504,17 @@ method = GA(pop_size=pop_size,
Now we are ready to minimize the NDP problem using the GA method we defined.
<!-- #endregion -->
+<div style="background-color:#a6ffa6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Instructions:</b> Run the GA for 3 minutes initially to observe how the results converge and to understand the process of obtaining the final solution. Once you have familiarized yourself with the mechanism and the behavior of the algorithm, extend the maximum computation time to 10 minutes. Use the results from this extended run as the foundation for addressing the questions outlined in the report. </p></div>
+
<!-- #region id="0491cc69" -->
-<div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> termination is set here as a keyword argument (see note above).</p></div>
+<div style="background-color:#ffa500; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> Maximum computation time (termination criteria) is set here as a keyword argument.</p></div>
<!-- #endregion -->
```python pycharm={"name": "#%%\n"}
opt_results = minimize(problem,
method,
- termination=("time", "00:05:00"), #5 minute maximum computation time
+ termination=("time", "00:03:00"), #this is maximum computation time
seed=7,
save_history=True,
verbose=True,
diff --git a/synced_files/GA_2_5/old_Analysis_GA_solution.py b/synced_files/GA_2_5/old_Analysis_GA_solution.py
index b93e4757c3579c13051b4fd78f417e0d7e27494d..e64e370d20dbcb5ca10a724426fb0e786f4dc558 100644
--- a/synced_files/GA_2_5/old_Analysis_GA_solution.py
+++ b/synced_files/GA_2_5/old_Analysis_GA_solution.py
@@ -30,9 +30,6 @@
# *[CEGM1000 MUDE](http://mude.citg.tudelft.nl/): Week 2.5. For: 15 December, 2023.*
#
-# %% [markdown] id="0491cc69"
-# <div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> during the in-class session some of the confusion was caused by code issues. Comments relevant to the code and notebooks as-used in the Friday in-class session are provided in boxes like this.</p></div>
-
# %% [markdown] pycharm={"name": "#%% md\n"}
# ## Introduction
#
@@ -71,19 +68,7 @@ from pymoo.optimize import minimize
from pymoo.operators.sampling.rnd import BinaryRandomSampling
from pymoo.operators.crossover.hux import HalfUniformCrossover #
from pymoo.operators.mutation.bitflip import BitflipMutation
-
-# not used here but generally useful dependencies
-#from pymoo.core.problem import Problem
-#from pymoo.operators.mutation.pm import PolynomialMutation
-#from pymoo.operators.crossover.pntx import PointCrossover #
-#from pymoo.operators.crossover.sbx import SBX
-#from pymoo.operators.crossover.sbx import SimulatedBinaryCrossover
-#from pymoo.operators.crossover.pntx import Crossover
-#from pymoo.operators.repair.bounds_repair import BoundsRepair
-#from pymoo.core.repair import Repair
-
-# %% [markdown] id="0491cc69"
-# <div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> the intention with the commented lines above was to illustrate possible crossover methods you could use, but unfortunately the pymoo documentation was not clear enough to indicate how they could have been used. The methods <code>SinglePointCrossover</code> and <code>TwoPointCrossover</code> (n<em>not</em> listed above!) could have been easily used, as these methods require no additional keyword arguments and would have worked “out of the box;†they were also illustrated with examples in the online textbook. We apologize for this oversight on our part.</p></div>
+from pymoo.operators.crossover.pntx import PointCrossover #
# %%
# For visualization
@@ -327,17 +312,9 @@ def create_nd_matrix(ods_data, origins, destinations, nodes):
# %% [markdown] pycharm={"name": "#%% md\n"}
# Now that we have the required functions for reading and processing the data, let's define some problem parameters and prepare the input.
-# %% [markdown] id="0491cc69"
-# <div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> the variables <code>extension_max_no</code> and <code>timelimit</code> were both defined here, but never used. <code>extension_max_no</code> is similar to the variable in notebook A with the same name; here it is superceded below by <code>Budget</code>.</p></div>
-
# %% pycharm={"name": "#%%\n"}
# define parameters, case study (network) list and the directory where their files are
extension_factor = 1.5 # capacity after extension (1.5 means add 50%)
-extension_max_no = 20 # the number of links to add capacity to (simplified way of reprsenting a budget for investing)
-#it's the same to say that it's exactly this number of that this number is the max, that's because every investment brings travel time benefits
-#even if just one car circulates.
-timelimit = 300 # seconds
-beta = 2 # parameter to use in link travel time function (explained later)
networks = ['SiouxFalls']
networks_dir = os.getcwd() +'/input/TransportationNetworks'
@@ -358,17 +335,6 @@ links = list(net_data['capacity'].keys())
nodes = np.unique([list(edge) for edge in links])
fftts = net_data['free_flow']
-# auxiliary parameters (dict format) to keep the problem linear (capacities as parameters rather than variables)
-cap_normal = {(i, j): cap for (i, j), cap in net_data['capacity'].items()}
-cap_extend = {(i, j): cap * extension_factor for (i, j), cap in net_data['capacity'].items()}
-
-# origins and destinations
-dests = np.unique([dest for (orig, dest) in list(ods_data.keys())])
-origs = np.unique([orig for (orig, dest) in list(ods_data.keys())])
-
-# demand in node-destination form
-demand = create_nd_matrix(ods_data, origs, dests, nodes)
-
# %% [markdown]
# ## Network Display
# We will use the same function we used in the previous notebook to visualize the network.
@@ -497,39 +463,37 @@ class NDP(ElementwiseProblem):
# Now, let's initiate an instance of the problem based on the problem class we defined, and initiate the GA with its parameters. Note that depending on the problem size and the number of feasible links, you might need larger values for population and generation size to achieve good results or even feasible results. Of course this increases the computation times.
# %% [markdown] id="0491cc69"
-# <div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> population size <code>pop_size</code> was 10 originally. If you change this, you will see different results. This is problem-dependent!</p></div>
-
-# %% [markdown] id="0491cc69"
-# <div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). </p></div>
-
-# %% [markdown] id="0491cc69"
-# <div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>This:</b> <code>Budget</code> is the way the number of links was selected (unlike notebook A, which used <code>extension_max_no</code>). The initial value of 76 was trivial, and the solution converged quickly. Numbers between 10 and 40 would have produced <em>much</em> more interesting results (see solution explanation).</p></div>
+# <div style="background-color:#ffa500; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> population size <code>pop_size</code> is 10 originally. If you change this, you will see different results. This is problem-dependent!</p></div>
# %% pycharm={"name": "#%%\n"}
-Budget = 76
+extension_max_no = 40
pop_size = 10
# initiate an instance of the problem with max number of selected links as budget constraint
-problem = NDP(budget=Budget)
+problem = NDP(budget=extension_max_no)
# initiate the GA with parameters appropriate for binary variables
method = GA(pop_size=pop_size,
sampling=BinaryRandomSampling(),
mutation=BitflipMutation(),
crossover=HalfUniformCrossover()
+ # replace HalfUniformCrossover() by PointCrossover(2) for two-point crossover
)
# %% [markdown] pycharm={"name": "#%% md\n"}
# Now we are ready to minimize the NDP problem using the GA method we defined.
+# %% [markdown]
+# <div style="background-color:#a6ffa6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Instructions:</b> Run the GA for 3 minutes initially to observe how the results converge and to understand the process of obtaining the final solution. Once you have familiarized yourself with the mechanism and the behavior of the algorithm, extend the maximum computation time to 10 minutes. Use the results from this extended run as the foundation for addressing the questions outlined in the report. </p></div>
+
# %% [markdown] id="0491cc69"
-# <div style="background-color:#ffa6a6; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> termination is set here as a keyword argument (see note above).</p></div>
+# <div style="background-color:#ffa500; color: black; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px; width: 95%"><p><b>Note:</b> Maximum computation time (termination criteria) is set here as a keyword argument.</p></div>
# %% pycharm={"name": "#%%\n"}
opt_results = minimize(problem,
method,
- termination=("time", "00:05:00"), #5 minute maximum computation time
+ termination=("time", "00:03:00"), #this is maximum computation time
seed=7,
save_history=True,
verbose=True,
diff --git a/synced_files/Week_2_2/old/PA_2_1_solution_sympy.ipynb b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.ipynb
index 7231cc37fc57ec4e244babdce093cfeaecdaef70..104c3a06ac4b1dfc9cf1a34bde4a192fcb108995 100644
--- a/synced_files/Week_2_2/old/PA_2_1_solution_sympy.ipynb
+++ b/synced_files/Week_2_2/old/PA_2_1_solution_sympy.ipynb
@@ -404,7 +404,7 @@
},
{
"cell_type": "markdown",
- "id": "fb421a99",
+ "id": "0e6034c8",
"metadata": {},
"source": [
"You could also solve this problem using sympy! What would be the benefit of doing this? Check below how long it will take! You won't need timeit for this one..."
@@ -413,7 +413,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9d46ce25",
+ "id": "10b0eed5",
"metadata": {},
"outputs": [],
"source": [
@@ -423,7 +423,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "77d02ecd",
+ "id": "33730592",
"metadata": {},
"outputs": [],
"source": [
@@ -433,7 +433,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "4a7fd681",
+ "id": "2932c802",
"metadata": {},
"outputs": [],
"source": [
@@ -445,7 +445,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "dbd13d4f",
+ "id": "71e0ad8b",
"metadata": {},
"outputs": [],
"source": [
@@ -456,7 +456,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "b1f1a425",
+ "id": "662424a4",
"metadata": {},
"outputs": [],
"source": [
@@ -465,7 +465,7 @@
},
{
"cell_type": "markdown",
- "id": "695a0650",
+ "id": "fb9cb604",
"metadata": {},
"source": [
"What is the result for the 501th value using float values?"
@@ -474,7 +474,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "e76d2404",
+ "id": "577f7b77",
"metadata": {},
"outputs": [],
"source": [
@@ -514,7 +514,7 @@
},
{
"cell_type": "markdown",
- "id": "8ee71e48",
+ "id": "9e2c54ce",
"metadata": {},
"source": []
}
diff --git a/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb b/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb
index 98b673143acaa834a84a544c795f3b4949d8b665..2f7528c312fde9c369c80d5aa981164192abe6ff 100644
--- a/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb
+++ b/synced_files/Week_2_2/old/old_PA10_solution_sympy.ipynb
@@ -404,7 +404,7 @@
},
{
"cell_type": "markdown",
- "id": "f578e197",
+ "id": "62b145a8",
"metadata": {},
"source": [
"You could also solve this problem using sympy! What would be the benefit of doing this? Check below how long it will take! You won't need timeit for this one..."
@@ -413,7 +413,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "48a54a23",
+ "id": "7fdb91f6",
"metadata": {},
"outputs": [],
"source": [
@@ -423,7 +423,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "d8c6c3e1",
+ "id": "c2e7d800",
"metadata": {},
"outputs": [],
"source": [
@@ -433,7 +433,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "66de3bb0",
+ "id": "9f9c00c6",
"metadata": {},
"outputs": [],
"source": [
@@ -445,7 +445,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "84949595",
+ "id": "2173c512",
"metadata": {},
"outputs": [],
"source": [
@@ -456,7 +456,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "8f366c94",
+ "id": "0c87007b",
"metadata": {},
"outputs": [],
"source": [
@@ -465,7 +465,7 @@
},
{
"cell_type": "markdown",
- "id": "7d0e9b1a",
+ "id": "51301705",
"metadata": {},
"source": [
"What is the result for the 501th value using float values?"
@@ -474,7 +474,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "4c2decbe",
+ "id": "b96f5b71",
"metadata": {},
"outputs": [],
"source": [
@@ -514,7 +514,7 @@
},
{
"cell_type": "markdown",
- "id": "f8a738aa",
+ "id": "0d5c33b6",
"metadata": {},
"source": []
}