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Streaming Videos Algorithm Runner

This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.

Implemented Algorithms:

  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.

It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.

Prerequisites

  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip: 
    pip install tk

Running the Application

  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:

    cd src

  2. Run the main script: Execute the main.py script:

    python main.py

  3. Use the GUI:

    A graphical user interface will appear.

    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.

Running Tests (Optional)

WARNING: This is a very costly operation. It will take a long time to complete.

  1. Navigate to the test directory: In the terminal, change the current directory to the test directory: 
    cdtest
  2. Run the test script: Execute the test.py script: 
    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.

License

This project is distributed under the MIT License.

This assignment was developed by:

About

A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.

sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894

Topics

algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Streaming Videos Algorithm Runner
This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.
Implemented Algorithms:
  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.
It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.
Prerequisites
  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip:

    pip install tk
Running the Application
  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:
    cd src
  2. Run the main script: Execute the main.py script:
    python main.py
  3. Use the GUI:
    A graphical user interface will appear.
    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.
Running Tests (Optional)
WARNING: This is a very costly operation. It will take a long time to complete.
  1. Navigate to the test directory: In the terminal, change the current directory to the test directory:

    cdtest
  2. Run the test script: Execute the test.py script:

    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter
Resources
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MIT license
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1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Streaming Videos Algorithm Runner
This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.
Implemented Algorithms:
  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.
It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.
Prerequisites
  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip:

    pip install tk
Running the Application
  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:
    cd src
  2. Run the main script: Execute the main.py script:
    python main.py
  3. Use the GUI:
    A graphical user interface will appear.
    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.
Running Tests (Optional)
WARNING: This is a very costly operation. It will take a long time to complete.
  1. Navigate to the test directory: In the terminal, change the current directory to the test directory:

    cdtest
  2. Run the test script: Execute the test.py script:

    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Streaming Videos Algorithm Runner
This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.
Implemented Algorithms:
  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.
It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.
Prerequisites
  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip:

    pip install tk
Running the Application
  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:
    cd src
  2. Run the main script: Execute the main.py script:
    python main.py
  3. Use the GUI:
    A graphical user interface will appear.
    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.
Running Tests (Optional)
WARNING: This is a very costly operation. It will take a long time to complete.
  1. Navigate to the test directory: In the terminal, change the current directory to the test directory:

    cdtest
  2. Run the test script: Execute the test.py script:

    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter
Resources
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Streaming Videos Algorithm Runner
This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.
Implemented Algorithms:
  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.
It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.
Prerequisites
  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip:

    pip install tk
Running the Application
  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:
    cd src
  2. Run the main script: Execute the main.py script:
    python main.py
  3. Use the GUI:
    A graphical user interface will appear.
    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.
Running Tests (Optional)
WARNING: This is a very costly operation. It will take a long time to complete.
  1. Navigate to the test directory: In the terminal, change the current directory to the test directory:

    cdtest
  2. Run the test script: Execute the test.py script:

    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter
Resources
Readme
MIT license
Activity
Stars
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Watchers
1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Streaming Videos Algorithm Runner
This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.
Implemented Algorithms:
  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.
It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.
Prerequisites
  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip:

    pip install tk
Running the Application
  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:
    cd src
  2. Run the main script: Execute the main.py script:
    python main.py
  3. Use the GUI:
    A graphical user interface will appear.
    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.
Running Tests (Optional)
WARNING: This is a very costly operation. It will take a long time to complete.
  1. Navigate to the test directory: In the terminal, change the current directory to the test directory:

    cdtest
  2. Run the test script: Execute the test.py script:

    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Streaming Videos Algorithm Runner
This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.
Implemented Algorithms:
  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.
It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.
Prerequisites
  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip:

    pip install tk
Running the Application
  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:
    cd src
  2. Run the main script: Execute the main.py script:
    python main.py
  3. Use the GUI:
    A graphical user interface will appear.
    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.
Running Tests (Optional)
WARNING: This is a very costly operation. It will take a long time to complete.
  1. Navigate to the test directory: In the terminal, change the current directory to the test directory:

    cdtest
  2. Run the test script: Execute the test.py script:

    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
Streaming Videos Algorithm Runner
This project implements and evaluates several metaheuristic algorithms for optimization problems. It includes a graphical user interface (GUI) for easy algorithm execution and testing scripts for performance evaluation.
Implemented Algorithms:
  • Hill Climbing: A simple iterative improvement algorithm that moves to the best neighboring solution.
  • Simulated Annealing: A probabilistic technique that escapes local optima by accepting worse solutions with a decreasing probability.
  • Local Search: A general iterative improvement algorithm that explores the neighborhood of a solution.
  • Tabu Search: A metaheuristic that avoids cycling by keeping a tabu list of recently visited solutions.
  • Iterated Local Search: An algorithm that improves local search by perturbing the solution and reapplying local search.
  • Genetic Algorithm: A population-based algorithm inspired by natural selection, using crossover and mutation.
It is strongly advised to run this project within an Integrated Development Environment (IDE) such as PyCharm, VSCode, or similar. This will help manage dependencies and execute the scripts.
Prerequisites
  • Python 3.x installed.
  • Required Python packages: tkinter. Install using pip:

    pip install tk
Running the Application
  1. Navigate to the src directory: Open a terminal or command prompt and change the current directory to the src directory of the project:
    cd src
  2. Run the main script: Execute the main.py script:
    python main.py
  3. Use the GUI:
    A graphical user interface will appear.
    • Algorithm Selection: Choose the algorithm to run from the dropdown.
    • Data File Selection: Enter the path to the data file or use the "Browse" button. (Data files are in the data directory).
    • Algorithm Parameters: Input the parameters for the selected algorithm. Default values are provided.
    • Run Algorithm: Click "Run Algorithm" to execute the algorithm.
    • Results: The initial and final solution scores will be displayed.
Running Tests (Optional)
WARNING: This is a very costly operation. It will take a long time to complete.
  1. Navigate to the test directory: In the terminal, change the current directory to the test directory:

    cdtest
  2. Run the test script: Execute the test.py script:

    python test.py
  3. View Test Results:
    • Algorithm output data is written to text files in the test/results/run_{datasetNumber} directory.
License
This project is distributed under the MIT License.
This assignment was developed by:
About
A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.
sigarra.up.pt/feup/en/UCURR_GERAL.FICHA_UC_VIEW?pv_ocorrencia_id=541894
Topics
algorithm-comparisondata-sciencegenetic-algorithmgoogle-hash-codegraphical-interfacehash-code-2017hill-climbingiterated-local-searchlocal-searchmetaheuristicsoptimizationpythonsimulated-annealingtabu-searchtkinter
Resources
Readme
MIT license
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Contributors
Languages