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Evolutionary algorithms library for the web.

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Introduction • Installation • Usage • Roadmap • Contributing • Authors • License

📚 Introduction

Evolutionary computing is one of the main techniques nowadays for solving complex optimization problems. This library provides with the basic structure for implementing the most common evolutionary algorithms, such as genetic algorithms.

drawing

Evolutionary algorithms basic structure

Evolutionary algorithms are composed basically by four elements:

  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.

This framework is going to provide the most common techniques for each component.

🔧 Installation

Currently project is under development (no stable version released ⚠️), but it is going to be installed through npm:

npm install genetics-js

🧬 Usage

No major versions have been released, so only Individuals creation is implemented:

importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]

🌠 Roadmap

The roadmap is strictly determined by the operations that are going to be implemented:

  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.

👐 Contributing

You can report a bug, or request a feature with an issue:

Any help would be welcome 😄.

💪 Authors

📝 License

This project is licensed under the MIT License

About

Evolutionary algorithms library for the web 🧬

geneticsjs.wordpress.com/

Resources

Readme
MIT license

Contributing

Contributing
Activity
Custom properties

Stars

0 stars

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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genetics.js logo

Evolutionary algorithms library for the web.

GitHubCircleCI branchCoverage StatusOpen sourceGitHub tag (latest SemVer)WebsiteTwitter Follow
Introduction •
Installation •
Usage •
Roadmap •
Contributing •
Authors •
License
📚 Introduction
Evolutionary computing is one of the main techniques nowadays
for solving complex optimization problems. This library provides
with the basic structure for implementing the most common evolutionary
algorithms, such as genetic algorithms.
drawing
Evolutionary algorithms basic structure
Evolutionary algorithms are composed basically by four elements:
  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.
This framework is going to provide the most common techniques for each component.
🔧 Installation
Currently project is under development (no stable version released ⚠️), but it is going to be installed
through npm:
npm install genetics-js

🧬 Usage
No major versions have been released, so only Individuals creation is implemented:
importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]
🌠 Roadmap
The roadmap is strictly determined by the operations that are going to be
implemented:
  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.
👐 Contributing
You can report a bug, or request a feature with an issue:
Any help would be welcome 😄.
💪 Authors
📝 License
This project is licensed under the MIT License
About
Evolutionary algorithms library for the web 🧬
geneticsjs.wordpress.com/
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 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('^' + ".*" + '

Skip to content
BranchesTags
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genetics.js logo

Evolutionary algorithms library for the web.

GitHubCircleCI branchCoverage StatusOpen sourceGitHub tag (latest SemVer)WebsiteTwitter Follow
Introduction •
Installation •
Usage •
Roadmap •
Contributing •
Authors •
License
📚 Introduction
Evolutionary computing is one of the main techniques nowadays
for solving complex optimization problems. This library provides
with the basic structure for implementing the most common evolutionary
algorithms, such as genetic algorithms.
drawing
Evolutionary algorithms basic structure
Evolutionary algorithms are composed basically by four elements:
  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.
This framework is going to provide the most common techniques for each component.
🔧 Installation
Currently project is under development (no stable version released ⚠️), but it is going to be installed
through npm:
npm install genetics-js

🧬 Usage
No major versions have been released, so only Individuals creation is implemented:
importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]
🌠 Roadmap
The roadmap is strictly determined by the operations that are going to be
implemented:
  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.
👐 Contributing
You can report a bug, or request a feature with an issue:
Any help would be welcome 😄.
💪 Authors
📝 License
This project is licensed under the MIT License
About
Evolutionary algorithms library for the web 🧬
geneticsjs.wordpress.com/
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
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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('^' + ".*" + '

Skip to content
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genetics.js logo

Evolutionary algorithms library for the web.

GitHubCircleCI branchCoverage StatusOpen sourceGitHub tag (latest SemVer)WebsiteTwitter Follow
Introduction •
Installation •
Usage •
Roadmap •
Contributing •
Authors •
License
📚 Introduction
Evolutionary computing is one of the main techniques nowadays
for solving complex optimization problems. This library provides
with the basic structure for implementing the most common evolutionary
algorithms, such as genetic algorithms.
drawing
Evolutionary algorithms basic structure
Evolutionary algorithms are composed basically by four elements:
  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.
This framework is going to provide the most common techniques for each component.
🔧 Installation
Currently project is under development (no stable version released ⚠️), but it is going to be installed
through npm:
npm install genetics-js

🧬 Usage
No major versions have been released, so only Individuals creation is implemented:
importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]
🌠 Roadmap
The roadmap is strictly determined by the operations that are going to be
implemented:
  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.
👐 Contributing
You can report a bug, or request a feature with an issue:
Any help would be welcome 😄.
💪 Authors
📝 License
This project is licensed under the MIT License
About
Evolutionary algorithms library for the web 🧬
geneticsjs.wordpress.com/
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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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" + '

Skip to content
BranchesTags
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genetics.js logo

Evolutionary algorithms library for the web.

GitHubCircleCI branchCoverage StatusOpen sourceGitHub tag (latest SemVer)WebsiteTwitter Follow
Introduction •
Installation •
Usage •
Roadmap •
Contributing •
Authors •
License
📚 Introduction
Evolutionary computing is one of the main techniques nowadays
for solving complex optimization problems. This library provides
with the basic structure for implementing the most common evolutionary
algorithms, such as genetic algorithms.
drawing
Evolutionary algorithms basic structure
Evolutionary algorithms are composed basically by four elements:
  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.
This framework is going to provide the most common techniques for each component.
🔧 Installation
Currently project is under development (no stable version released ⚠️), but it is going to be installed
through npm:
npm install genetics-js

🧬 Usage
No major versions have been released, so only Individuals creation is implemented:
importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]
🌠 Roadmap
The roadmap is strictly determined by the operations that are going to be
implemented:
  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.
👐 Contributing
You can report a bug, or request a feature with an issue:
Any help would be welcome 😄.
💪 Authors
📝 License
This project is licensed under the MIT License
About
Evolutionary algorithms library for the web 🧬
geneticsjs.wordpress.com/
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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Contributors
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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('^' + ".*" + '

Skip to content
BranchesTags
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genetics.js logo

Evolutionary algorithms library for the web.

GitHubCircleCI branchCoverage StatusOpen sourceGitHub tag (latest SemVer)WebsiteTwitter Follow
Introduction •
Installation •
Usage •
Roadmap •
Contributing •
Authors •
License
📚 Introduction
Evolutionary computing is one of the main techniques nowadays
for solving complex optimization problems. This library provides
with the basic structure for implementing the most common evolutionary
algorithms, such as genetic algorithms.
drawing
Evolutionary algorithms basic structure
Evolutionary algorithms are composed basically by four elements:
  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.
This framework is going to provide the most common techniques for each component.
🔧 Installation
Currently project is under development (no stable version released ⚠️), but it is going to be installed
through npm:
npm install genetics-js

🧬 Usage
No major versions have been released, so only Individuals creation is implemented:
importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]
🌠 Roadmap
The roadmap is strictly determined by the operations that are going to be
implemented:
  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.
👐 Contributing
You can report a bug, or request a feature with an issue:
Any help would be welcome 😄.
💪 Authors
📝 License
This project is licensed under the MIT License
About
Evolutionary algorithms library for the web 🧬
geneticsjs.wordpress.com/
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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Contributors
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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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genetics.js logo

Evolutionary algorithms library for the web.

GitHubCircleCI branchCoverage StatusOpen sourceGitHub tag (latest SemVer)WebsiteTwitter Follow
Introduction •
Installation •
Usage •
Roadmap •
Contributing •
Authors •
License
📚 Introduction
Evolutionary computing is one of the main techniques nowadays
for solving complex optimization problems. This library provides
with the basic structure for implementing the most common evolutionary
algorithms, such as genetic algorithms.
drawing
Evolutionary algorithms basic structure
Evolutionary algorithms are composed basically by four elements:
  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.
This framework is going to provide the most common techniques for each component.
🔧 Installation
Currently project is under development (no stable version released ⚠️), but it is going to be installed
through npm:
npm install genetics-js

🧬 Usage
No major versions have been released, so only Individuals creation is implemented:
importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]
🌠 Roadmap
The roadmap is strictly determined by the operations that are going to be
implemented:
  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.
👐 Contributing
You can report a bug, or request a feature with an issue:
Any help would be welcome 😄.
💪 Authors
📝 License
This project is licensed under the MIT License
About
Evolutionary algorithms library for the web 🧬
geneticsjs.wordpress.com/
Resources
Readme
MIT license
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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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Evolutionary algorithms library for the web.

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Introduction •
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Roadmap •
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📚 Introduction
Evolutionary computing is one of the main techniques nowadays
for solving complex optimization problems. This library provides
with the basic structure for implementing the most common evolutionary
algorithms, such as genetic algorithms.
drawing
Evolutionary algorithms basic structure
Evolutionary algorithms are composed basically by four elements:
  • Individuals: Represent possible solutions of our problem in a determinate search space.
  • Mutation: Mutation operator alterates one individual.
  • Recombination: Recombination operator takes two parents and creates the offspring.
  • Parent selection: Selection of the best parents that are going to be reproduced in the next generation.
  • Survivor selection: Selection of the offspring and parents that are going to be the next generation.
This framework is going to provide the most common techniques for each component.
🔧 Installation
Currently project is under development (no stable version released ⚠️), but it is going to be installed
through npm:
npm install genetics-js

🧬 Usage
No major versions have been released, so only Individuals creation is implemented:
importGeneticsfrom'genetics-js';const{ BinaryIndividual }=Genetics.individual;letindividual=newBinaryIndividual('001100');individual.genotype// [false, false, true, true, false, false]
🌠 Roadmap
The roadmap is strictly determined by the operations that are going to be
implemented:
  • v0.1.0: Implementation of individuals.
  • v0.2.0: Implementation of mutation operators.
  • v0.3.0: Implementation of recombination operators.
  • v0.4.0: Implementation of parent selection methods.
  • v0.5.0: Implementation of survivor selection methods.
  • v0.6.0: Implementation of population and offspring management.
  • v0.7.0: Implementation of common evolutionary algorithms with fixed configurations.
👐 Contributing
You can report a bug, or request a feature with an issue:
Any help would be welcome 😄.
💪 Authors
📝 License
This project is licensed under the MIT License
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Evolutionary algorithms library for the web 🧬
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