Search Results for "genetic algorithm software"
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Showing 77 open source projects for "genetic algorithm software"
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. Written primarily in... -
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pgapack, the parallel genetic algorithm library is a powerfull genetic algorithm library by D. Levine, Mathematics and Computer Science Division Argonne National Laboratory. The library is written in C. PGAPy wraps this library for use with Python.
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zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt.... -
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Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and... -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
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DualPipe
A bidirectional pipeline parallelism algorithm
DualPipe is a bidirectional pipeline parallelism algorithm open-sourced by DeepSeek, introduced in their DeepSeek-V3 technical framework. The main goal of DualPipe is to maximize overlap between computation and communication phases during distributed training, thus reducing idle GPU time (i.e. “pipeline bubbles”) and improving cluster efficiency. Traditional pipeline parallelism methods (e.g. 1F1B or staggered pipelining) leave gaps because forward and backward phases can’t fully overlap... -
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Pythonic Data Structures and Algorithms
Minimal examples of data structures and algorithms in Python
The Pythonic Data Structures and Algorithms repository by keon is a hands-on collection of implementations of classical data structures and algorithms written in Python. It offers working, often well-commented code for many standard algorithmic problems — from sorting/searching to graph algorithms, dynamic programming, data structures, and more — making it a valuable resource for learning and reference. For students preparing for technical interviews, self-learners brushing up on... -
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PRML
PRML algorithms implemented in Python
PRML repository is a respected and well-maintained project that implements the foundational algorithms from the famous textbook Pattern Recognition and Machine Learning by Christopher M. Bishop, providing a practical and accessible Python reference for both students and professionals. Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM... -
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TextDistance
Compute distance between sequences
Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). TextDistance show benchmarks... -
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Recursive Language Models
General plug-and-play inference library for Recursive Language Models
RLM (short for Reinforcement Learning Models) is a modular framework that makes it easier to build, train, evaluate, and deploy reinforcement learning (RL) agents across a wide range of environments and tasks. It provides a consistent API that abstracts away many of the repetitive engineering patterns in RL research and application work, letting developers focus on modeling, experimentation, and fine-tuning rather than infrastructure plumbing. Within the framework, you can define custom... -
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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. You can run it as a command-line tool or call it as a library via FormatCode / FormatFile, making it easy to embed in... -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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Evolutionary Algorithm
Evolutionary Algorithm using Python
...Users can explore basic genetic algorithm setups, match phrase examples, pathfinding challenges, and microbial GA variants, as well as evolution strategy approaches like NES. The project also links classical evolutionary approaches with neural networks, illustrating how evolution can be used for model training in reinforcement learning and supervised contexts. -
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Instagram CLI
Become a 10x Instagrammer and Escape Brainrot with Instagram CLI
Instagram: the app you open to send one message… and suddenly 30 minutes are gone. We all know the cycle — and we finally had enough. What if Instagram had a quick fix? A productivity-friendly mode that keeps your connections, but gives your attention span a fighting chance? No algorithm traps. No reel vortex. No dopamine casino. Just conversations and updates, on your terms. Instagram CLI is a minimal, fast, keyboard-native way to stay connected without getting cooked by social media. -
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TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing. -
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Robin-Stocks API Library
This is a library to use with Robinhood Financial App
This is a library to use with Robinhood Financial App. It currently supports trading crypto-currencies, options, and stocks. In addition, it can be used to get real-time ticker information, assess the performance of your portfolio, and can also get tax documents, total dividends paid, and more. The code is simple to use, easy to understand, and easy to modify. With this library, you can view information on stocks, options, and cryptocurrencies in real-time, create your own robo-investor or... -
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Alphafold
Open source code for AlphaFold
This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper. Please also refer to the Supplementary Information for a detailed description of the method. You can use... -
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Sudoku Maker is a generator for Sudoku number puzzles. It uses a genetic algorithm internally, so it can serve as an introduction to genetic algorithms. The generated Sudokus are usually very hard to solve -- good for getting rid of a Sudoku addiction.
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GFPGAN
GFPGAN aims at developing Practical Algorithms
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing... -
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AlphaTensor
AI discovers faster, efficient algorithms for matrix multiplication
AlphaTensor, developed by Google DeepMind, is the research codebase accompanying the 2022 Nature publication “Discovering faster matrix multiplication algorithms with reinforcement learning.” The project demonstrates how reinforcement learning can be used to automatically discover efficient algorithms for matrix multiplication — a fundamental operation in computer science and numerical computation. The repository is organized into four main components: algorithms, benchmarking,... -
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no... -
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Reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI
Reinforcement-learning is a widely used educational repository that provides implementations, exercises, and solutions for a broad range of reinforcement learning algorithms, designed to complement foundational texts and courses in the field. The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so... -
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scikit-opt
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
Genetic algorithm operators can be replaced through user-defined functions or subclassing. Runs may continue from earlier iterations instead of restarting. Vectorization, threading, multiprocessing, caching, examples, and plotting support help users experiment with performance and convergence. -
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algo
50 Code Implementations You Must Know About Data Structures
algo is an educational repository containing practical implementations of essential data structures and algorithms. It organizes roughly fifty core exercises by topic rather than presenting a single reusable software library. The material covers arrays, linked lists, stacks, queues, recursion, sorting, binary search, hash tables, strings, trees, heaps, and graphs. More advanced examples address backtracking, divide-and-conquer methods, dynamic programming, shortest paths, topological... -
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bioweb
polyglot language framework to analyze genetic data
polyglot framework using Python/C++/JavaScript to fast develop applications to analyze biological sequences
