@volpe-framework

VolPE Framework

README.md

VolPE

Volunteer computing Platform for Evolutionary algorithms

VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.

The Architecture

VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.

  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.

Key Features

  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.

Technical Stack

  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks

Getting Started for Volunteers

  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git
  1. Install Dependencies:
pip install grpcio deap tsplib95
  1. Run the Container: By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.

Contributing

We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

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  1. volpe-applicationvolpe-applicationPublic

    Volunteer computing Platform for Evolutionary Algorithms

    Go 3 1

  2. volpe-container-pyvolpe-container-pyPublic

    Python sample repository Dockerfile for VolPE

    Python 4

  3. volpe-protobufvolpe-protobufPublic

    Protobuf repository for the VolPE project.

    Makefile

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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" + '

Skip to content
@volpe-framework

VolPE Framework

README.md
VolPE
Volunteer computing Platform for Evolutionary algorithms
VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.
The Architecture
VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.
  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.
Key Features
  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.
Technical Stack
  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks
Getting Started for Volunteers
  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git

  1. Install Dependencies:
pip install grpcio deap tsplib95

  1. Run the Container:
By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.
Contributing
We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

Pinned
Loading
  1. volpe-applicationvolpe-applicationPublic
    
Volunteer computing Platform for Evolutionary Algorithms

    Go
3

1

  2. volpe-container-pyvolpe-container-pyPublic
    
Python sample repository Dockerfile for VolPE

    Python
4

  3. volpe-protobufvolpe-protobufPublic
    
Protobuf repository for the VolPE project.

    Makefile

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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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@volpe-framework

VolPE Framework

README.md
VolPE
Volunteer computing Platform for Evolutionary algorithms
VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.
The Architecture
VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.
  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.
Key Features
  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.
Technical Stack
  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks
Getting Started for Volunteers
  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git

  1. Install Dependencies:
pip install grpcio deap tsplib95

  1. Run the Container:
By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.
Contributing
We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

Pinned
Loading
  1. volpe-applicationvolpe-applicationPublic
    
Volunteer computing Platform for Evolutionary Algorithms

    Go
3

1

  2. volpe-container-pyvolpe-container-pyPublic
    
Python sample repository Dockerfile for VolPE

    Python
4

  3. volpe-protobufvolpe-protobufPublic
    
Protobuf repository for the VolPE project.

    Makefile

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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
@volpe-framework

VolPE Framework

README.md
VolPE
Volunteer computing Platform for Evolutionary algorithms
VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.
The Architecture
VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.
  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.
Key Features
  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.
Technical Stack
  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks
Getting Started for Volunteers
  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git

  1. Install Dependencies:
pip install grpcio deap tsplib95

  1. Run the Container:
By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.
Contributing
We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

Pinned
Loading
  1. volpe-applicationvolpe-applicationPublic
    
Volunteer computing Platform for Evolutionary Algorithms

    Go
3

1

  2. volpe-container-pyvolpe-container-pyPublic
    
Python sample repository Dockerfile for VolPE

    Python
4

  3. volpe-protobufvolpe-protobufPublic
    
Protobuf repository for the VolPE project.

    Makefile

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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
@volpe-framework

VolPE Framework

README.md
VolPE
Volunteer computing Platform for Evolutionary algorithms
VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.
The Architecture
VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.
  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.
Key Features
  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.
Technical Stack
  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks
Getting Started for Volunteers
  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git

  1. Install Dependencies:
pip install grpcio deap tsplib95

  1. Run the Container:
By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.
Contributing
We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

Pinned
Loading
  1. volpe-applicationvolpe-applicationPublic
    
Volunteer computing Platform for Evolutionary Algorithms

    Go
3

1

  2. volpe-container-pyvolpe-container-pyPublic
    
Python sample repository Dockerfile for VolPE

    Python
4

  3. volpe-protobufvolpe-protobufPublic
    
Protobuf repository for the VolPE project.

    Makefile

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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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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@volpe-framework

VolPE Framework

README.md
VolPE
Volunteer computing Platform for Evolutionary algorithms
VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.
The Architecture
VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.
  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.
Key Features
  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.
Technical Stack
  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks
Getting Started for Volunteers
  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git

  1. Install Dependencies:
pip install grpcio deap tsplib95

  1. Run the Container:
By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.
Contributing
We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

Pinned
Loading
  1. volpe-applicationvolpe-applicationPublic
    
Volunteer computing Platform for Evolutionary Algorithms

    Go
3

1

  2. volpe-container-pyvolpe-container-pyPublic
    
Python sample repository Dockerfile for VolPE

    Python
4

  3. volpe-protobufvolpe-protobufPublic
    
Protobuf repository for the VolPE project.

    Makefile

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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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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
@volpe-framework

VolPE Framework

README.md
VolPE
Volunteer computing Platform for Evolutionary algorithms
VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.
The Architecture
VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.
  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.
Key Features
  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.
Technical Stack
  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks
Getting Started for Volunteers
  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git

  1. Install Dependencies:
pip install grpcio deap tsplib95

  1. Run the Container:
By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.
Contributing
We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

Pinned
Loading
  1. volpe-applicationvolpe-applicationPublic
    
Volunteer computing Platform for Evolutionary Algorithms

    Go
3

1

  2. volpe-container-pyvolpe-container-pyPublic
    
Python sample repository Dockerfile for VolPE

    Python
4

  3. volpe-protobufvolpe-protobufPublic
    
Protobuf repository for the VolPE project.

    Makefile

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Showing 10 of 11 repositories
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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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@volpe-framework

VolPE Framework

README.md
VolPE
Volunteer computing Platform for Evolutionary algorithms
VolPE (Volunteer Computing Platform for Evolutionary Algorithms) is a distributed platform designed to solve complex optimization problems by harnessing the collective power of volunteer nodes. By utilizing a containerized Island Model, VolPE allows large-scale evolutionary experiments to run across heterogeneous environments, effectively turning a network of contributors into a massive parallel supercomputer.
The Architecture
VolPE operates on a decentralized architecture where each participant runs a VolPE Worker Application. The worker applications runs containers for problems assigned to the worker. These containers act as isolated evolutionary ecosystems (islands) that evolve populations locally and periodically communicate with an orchestrator to share genetic material.
  • Orchestrator/Master: Manages the lifecycle of experiments, synchronizes generations, and handles the migration of high-fitness individuals between nodes.
  • Workers: Perform the heavy lifting—selection, crossover, and mutation—using high-performance libraries like DEAP and Python Array.
Key Features
  • Low Overhead: Built with gRPC and Protobuf for high-performance, low-latency communication.
  • Memory Efficient: Uses the native Python array module and struct serialization to minimize the memory footprint on volunteer machines.
  • Adaptive Evolution: Supports dynamic noise and diversity coefficients that adjust based on the convergence rate of the local population.
  • Agnostic Design: While optimized for problems like the Traveling Salesperson Problem (TSP), the platform is designed to handle any optimization task that can be represented as a bitstring, permutation, or real-valued vector.
Technical Stack
  • Core Logic:DEAP (Distributed Evolutionary Algorithms in Python)
  • Communication: gRPC / Protocol Buffers
  • Data Handling: Python array and struct for binary-level efficiency
  • Problem Format: Support for standard tsplib95 benchmarks
Getting Started for Volunteers
  1. Clone the Repository:
git clone https://github.com/VolPE-Org/volpe-container.git

  1. Install Dependencies:
pip install grpcio deap tsplib95

  1. Run the Container:
By default, the container listens on port 8081.
python volpe_container.py

Once your container is running, it will wait for the VolPE Orchestrator to assign a seed population and start the evolutionary process.
Contributing
We are always looking for contributors to help optimize the migration protocols and expand the library of supported evolutionary operators. If you are interested in distributed systems or genetic algorithms, feel free to submit a PR or join our community discussions.

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  1. volpe-applicationvolpe-applicationPublic
    
Volunteer computing Platform for Evolutionary Algorithms

    Go
3

1

  2. volpe-container-pyvolpe-container-pyPublic
    
Python sample repository Dockerfile for VolPE

    Python
4

  3. volpe-protobufvolpe-protobufPublic
    
Protobuf repository for the VolPE project.

    Makefile

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