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Master Thesis | Optimizer Framework

This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.

Table of Contents

Installation

The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:

 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt

Usage

Running the example code can be done by running the following command:

 python main.py

Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).

License

About

Generic game optimizer framework built for my Master's Thesis

joshuamoelans.github.io/thesis/

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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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Master Thesis | Optimizer Framework
This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.
Table of Contents
Installation
The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:
 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt
Usage
Running the example code can be done by running the following command:
 python main.py
Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).
License
About
Generic game optimizer framework built for my Master's Thesis
joshuamoelans.github.io/thesis/
Resources
Readme
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1 watching
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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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Master Thesis | Optimizer Framework
This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.
Table of Contents
Installation
The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:
 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt
Usage
Running the example code can be done by running the following command:
 python main.py
Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).
License
About
Generic game optimizer framework built for my Master's Thesis
joshuamoelans.github.io/thesis/
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
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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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Master Thesis | Optimizer Framework
This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.
Table of Contents
Installation
The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:
 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt
Usage
Running the example code can be done by running the following command:
 python main.py
Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).
License
About
Generic game optimizer framework built for my Master's Thesis
joshuamoelans.github.io/thesis/
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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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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Master Thesis | Optimizer Framework
This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.
Table of Contents
Installation
The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:
 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt
Usage
Running the example code can be done by running the following command:
 python main.py
Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).
License
About
Generic game optimizer framework built for my Master's Thesis
joshuamoelans.github.io/thesis/
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Contributors
Languages

, '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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Master Thesis | Optimizer Framework
This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.
Table of Contents
Installation
The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:
 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt
Usage
Running the example code can be done by running the following command:
 python main.py
Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).
License
About
Generic game optimizer framework built for my Master's Thesis
joshuamoelans.github.io/thesis/
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
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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Master Thesis | Optimizer Framework
This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.
Table of Contents
Installation
The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:
 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt
Usage
Running the example code can be done by running the following command:
 python main.py
Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).
License
About
Generic game optimizer framework built for my Master's Thesis
joshuamoelans.github.io/thesis/
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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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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Master Thesis | Optimizer Framework
This repository holds the optimizer framework as proposed in my Master Thesis. It contains a generic optimizer class that can be used to implement various optimization algorithms for various use cases. One of these use cases is the Game Optimizer, which can be used to optimize the accompanying game made for this thesis.
Table of Contents
Installation
The in-game optimizer uses NLopt, so it is advised to install this in a virtual environment. To do this, run the following commands:
 python3 -m venv venv
source venv/bin/activate
pip install -r ./code/requirements.txt
Usage
Running the example code can be done by running the following command:
 python main.py
Adapting the parameters for the Game optimizer should be done inside the Parameters.py file. This allows you to set minimum and maximum values for the parameters, as well as the type. The optimization code itself is located in the GameOptimizer.py file. There you can set the NLopt optimization algorithm (currently using COBYLA) as well as the stopping criterion and objective function (by changing how the score_game(...) method calculates it).
License
About
Generic game optimizer framework built for my Master's Thesis
joshuamoelans.github.io/thesis/
Resources
Readme
Activity
Stars
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1 watching
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1 fork
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