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Genetically Evolved Neural Network

Results from approximating an arbitrary 1D function

What is this program ? Why is it there ?

This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.

This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.

What does it do ?

But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.

It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.

So this can be used for educational purposes.

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Genetically evolves neural networks to match a specific function

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genetic-algorithmmlneural-networknumpy

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Genetically Evolved Neural Network
Results from approximating an arbitrary 1D function
What is this program ? Why is it there ?
This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.
This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.
What does it do ?
But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.
It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.
So this can be used for educational purposes.
About
Genetically evolves neural networks to match a specific function
Topics
genetic-algorithmmlneural-networknumpy
Resources
Readme
GPL-3.0 license
Activity
Stars
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Watchers
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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Genetically Evolved Neural Network
Results from approximating an arbitrary 1D function
What is this program ? Why is it there ?
This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.
This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.
What does it do ?
But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.
It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.
So this can be used for educational purposes.
About
Genetically evolves neural networks to match a specific function
Topics
genetic-algorithmmlneural-networknumpy
Resources
Readme
GPL-3.0 license
Activity
Stars
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1 watching
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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");
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Genetically Evolved Neural Network
Results from approximating an arbitrary 1D function
What is this program ? Why is it there ?
This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.
This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.
What does it do ?
But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.
It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.
So this can be used for educational purposes.
About
Genetically evolves neural networks to match a specific function
Topics
genetic-algorithmmlneural-networknumpy
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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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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Genetically Evolved Neural Network
Results from approximating an arbitrary 1D function
What is this program ? Why is it there ?
This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.
This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.
What does it do ?
But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.
It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.
So this can be used for educational purposes.
About
Genetically evolves neural networks to match a specific function
Topics
genetic-algorithmmlneural-networknumpy
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Genetically Evolved Neural Network
Results from approximating an arbitrary 1D function
What is this program ? Why is it there ?
This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.
This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.
What does it do ?
But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.
It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.
So this can be used for educational purposes.
About
Genetically evolves neural networks to match a specific function
Topics
genetic-algorithmmlneural-networknumpy
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Genetically Evolved Neural Network
Results from approximating an arbitrary 1D function
What is this program ? Why is it there ?
This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.
This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.
What does it do ?
But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.
It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.
So this can be used for educational purposes.
About
Genetically evolves neural networks to match a specific function
Topics
genetic-algorithmmlneural-networknumpy
Resources
Readme
GPL-3.0 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("// 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");
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Genetically Evolved Neural Network
Results from approximating an arbitrary 1D function
What is this program ? Why is it there ?
This program is a small project designed to get used to DEAP, a genetic algorithm Python framework that greatly eases the developping process by providing a structure to populations, individuals and genetic operators handling.
This program also follows my multiple failed attempts at implementing the CoSyNE algorithm by hand in vanilla NumPy. In the future, I'm planning on modifying this program to incorporate the changes specific to the CoSyNE algorithm.
What does it do ?
But as for now, I have this program that can - with a simple genetic algorithm - evolve the weights and biases necessaries to build a network that will mimic the defined targetFunc for x in [0, 1] and y in [0, 1]. These intervals can easily be extended to include negative values as well but I'm not interested in doing that for now.
It can graph in live the evolution of the fitness, which is the same as the cost here, as we're trying to minimise it. Furthermore, when Ctrl+C is pressed or that the maximum numbre of generations n_gen is reached, it displays the best individual's weights and biases as well as the graphs of the best hof_size functions (by default 5) along with the targetFunc's graph.
So this can be used for educational purposes.
About
Genetically evolves neural networks to match a specific function
Topics
genetic-algorithmmlneural-networknumpy
Resources
Readme
GPL-3.0 license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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