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QAP with GA 🐒 Build Statuscodecov

Resolving Quadratic Assignment Problem with Genetic Algorithm

🚀 Getting Started

To run clone repo, go to src folder and run

python main.py

🔧 Config

In config.py you can find following configuration options:

INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.

📈 Visualization

Simulation

Visualization

Legend (how to read)

  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small

Both values are in context of particular distance and flow matrices

In short: thin green is better than thick red

Charts

Chart

🚚 Quadratic Assignment Problem

The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.

Source and more information: neos-guide.org

Dataset

Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW

Authors: S.W. Hadley, F. Rendl and H. Wolkowicz

Genetic Algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More

Important note

Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology

About

Resolving quadratic assignment problem with genetic algorithm

Topics

assignment-costgenetic-algorithmqap

Resources

Readme
Activity

Stars

14 stars

Watchers

0 watching

Forks

3 forks
Report repository

Contributors

Languages

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Repository files navigation
QAP with GA 🐒 Build Statuscodecov
Resolving Quadratic Assignment Problem with Genetic Algorithm
🚀 Getting Started
To run clone repo, go to src folder and run
python main.py

🔧 Config
In config.py you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.
📈 Visualization
Simulation
Visualization
Legend (how to read)
  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
Charts
Chart
🚚 Quadratic Assignment Problem
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset
Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Important note
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology
About
Resolving quadratic assignment problem with genetic algorithm
Topics
assignment-costgenetic-algorithmqap
Resources
Readme
Activity
Stars
14 stars
Watchers
0 watching
Forks
3 forks
Report repository
Contributors
Languages

, 'i');
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Repository files navigation
QAP with GA 🐒 Build Statuscodecov
Resolving Quadratic Assignment Problem with Genetic Algorithm
🚀 Getting Started
To run clone repo, go to src folder and run
python main.py

🔧 Config
In config.py you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.
📈 Visualization
Simulation
Visualization
Legend (how to read)
  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
Charts
Chart
🚚 Quadratic Assignment Problem
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset
Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Important note
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology
About
Resolving quadratic assignment problem with genetic algorithm
Topics
assignment-costgenetic-algorithmqap
Resources
Readme
Activity
Stars
14 stars
Watchers
0 watching
Forks
3 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
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highlight(document.body);
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
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Repository files navigation
QAP with GA 🐒 Build Statuscodecov
Resolving Quadratic Assignment Problem with Genetic Algorithm
🚀 Getting Started
To run clone repo, go to src folder and run
python main.py

🔧 Config
In config.py you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.
📈 Visualization
Simulation
Visualization
Legend (how to read)
  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
Charts
Chart
🚚 Quadratic Assignment Problem
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset
Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Important note
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology
About
Resolving quadratic assignment problem with genetic algorithm
Topics
assignment-costgenetic-algorithmqap
Resources
Readme
Activity
Stars
14 stars
Watchers
0 watching
Forks
3 forks
Report repository
Contributors
Languages

, 'i');
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'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
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cleanLinks();
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mutations.forEach(function(m) {
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
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Repository files navigation
QAP with GA 🐒 Build Statuscodecov
Resolving Quadratic Assignment Problem with Genetic Algorithm
🚀 Getting Started
To run clone repo, go to src folder and run
python main.py

🔧 Config
In config.py you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.
📈 Visualization
Simulation
Visualization
Legend (how to read)
  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
Charts
Chart
🚚 Quadratic Assignment Problem
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset
Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Important note
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology
About
Resolving quadratic assignment problem with genetic algorithm
Topics
assignment-costgenetic-algorithmqap
Resources
Readme
Activity
Stars
14 stars
Watchers
0 watching
Forks
3 forks
Report repository
Contributors
Languages

, 'i');
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setTimeout(tryTheater, 500);
}
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// Also try on player load
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observer.observe(document.body, { childList: true, subtree: true });
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}
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Repository files navigation
QAP with GA 🐒 Build Statuscodecov
Resolving Quadratic Assignment Problem with Genetic Algorithm
🚀 Getting Started
To run clone repo, go to src folder and run
python main.py

🔧 Config
In config.py you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.
📈 Visualization
Simulation
Visualization
Legend (how to read)
  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
Charts
Chart
🚚 Quadratic Assignment Problem
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset
Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Important note
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology
About
Resolving quadratic assignment problem with genetic algorithm
Topics
assignment-costgenetic-algorithmqap
Resources
Readme
Activity
Stars
14 stars
Watchers
0 watching
Forks
3 forks
Report repository
Contributors
Languages

, 'i');
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document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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Repository files navigation
QAP with GA 🐒 Build Statuscodecov
Resolving Quadratic Assignment Problem with Genetic Algorithm
🚀 Getting Started
To run clone repo, go to src folder and run
python main.py

🔧 Config
In config.py you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.
📈 Visualization
Simulation
Visualization
Legend (how to read)
  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
Charts
Chart
🚚 Quadratic Assignment Problem
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset
Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Important note
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology
About
Resolving quadratic assignment problem with genetic algorithm
Topics
assignment-costgenetic-algorithmqap
Resources
Readme
Activity
Stars
14 stars
Watchers
0 watching
Forks
3 forks
Report repository
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
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.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
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QAP with GA 🐒 Build Statuscodecov
Resolving Quadratic Assignment Problem with Genetic Algorithm
🚀 Getting Started
To run clone repo, go to src folder and run
python main.py

🔧 Config
In config.py you can find following configuration options:
INPUT_FILE = "had12.dat"
CROSSOVER_PROBABILITY = 0.7
MUTATION_PROBABILITY = 0.08
POPULATION_SIZE = 100
NUMBER_OF_GENERATIONS = 100
DRAW_VISUALIZATION = True
DRAW_CHART = True

Feel free to experiment with them.
📈 Visualization
Simulation
Visualization
Legend (how to read)
  • Red color of line means long distance, green one - short
  • Thick line means big value of flow (aka cost), thin one - small
Both values are in context of particular distance and flow matrices
In short: thin green is better than thick red
Charts
Chart
🚚 Quadratic Assignment Problem
The objective of the Quadratic Assignment Problem (QAP) is to assign n facilities to n locations in such a way as to minimize the assignment cost. The assignment cost is the sum, over all pairs, of the flow between a pair of facilities multiplied by the distance between their assigned locations.
Source and more information: neos-guide.org
Dataset
Dataset available in res/data are taken from http://anjos.mgi.polymtl.ca/qaplib/inst.html#HRW
Authors: S.W. Hadley, F. Rendl and H. Wolkowicz
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection. More
Important note
Some fragments of this implementation were inspired by code of mgr Filip Bachura from Wroclaw University of Science and Technology
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Resolving quadratic assignment problem with genetic algorithm
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assignment-costgenetic-algorithmqap
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