#Evolutionary-algorithms
Showing 46 of 46 repositories tagged #evolutionary-algorithms, ranked by stars
Open-source implementation of AlphaEvolve
Generate high-quality triangulated and polygonal art from images.
High-Performance Symbolic Regression in Python and Julia
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
A 2D Unity simulation in which cars learn to navigate themselves through different courses. The cars are steered by a feedforward neural network. The weights of the network are trained using a modified genetic algorithm.
Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.
A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization
Distributed High-Performance Symbolic Regression in Julia
The first open-source AI-driven tool for automatically generating system-level test cases (also known as fuzzing) for web/enterprise applications. Currently targeting whitebox and blackbox testing of Web APIs, like REST, GraphQL and RPC (e.g., gRPC and Thrift).
Automated modeling and machine learning framework FEDOT
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
LoongFlow: A Thinking & Learning Framework for Expert-Grade AI Agents.
The official code repository supporting the book, Grokking Artificial Intelligence Algorithms
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
A fast Evolution Strategy implementation in Python
A bare-bones Python library for quality diversity optimization.
A fast and flexible evolution engine for implementing artificial evolution and genetic programming techniques
Forex trading simulator environment for OpenAI Gym, observations contain the order status, performance and timeseries loaded from a CSV file containing rates and indicators. Work In Progress
Top 6 AI Paradigms Fighting for Survival in 2026
Yet another black-box optimization library for Python
Python framework for automated time series classification, regression and forecasting
Artificial life. Particles driven by neural networks, fighting on a grid and evolving through genetic algorithms.
Push Genetic Programming in Python.
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer (https://arxiv.org/abs/2008.02387) from NNAISENSE.
Finding an optimal Blackjack strategy using AI
Explorations into whether a transformer with RL can direct a genetic algorithm to converge faster
open source alpha evolve
Travel Time Optimization via Ant Colony and Genetic Evolution
Spare material for Computational Intelligence 01URROV @ Politecnico di Torino
Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatility, and effectiveness in finding global optima.
Implementation of Mind Evolution, Evolving Deeper LLM Thinking, from Deepmind
Evolution Strategy Library
An ecologically inspired multi-agent system. Agents are designed with neural network based decision making, and complex resource requirements.
This course covers the applied side of algorithmics in machine learning, with some deep learning and evolutionary algorithms thrown in as well.
Implementation of various evolutionary algorithms, starting with evolutionary strategies
Symbolic Generators for Complex Networks
An AI agent Learning to play Flappy Bird using Evolution Strategies and deep learning models.
Neuroevolution demo through TensorFlow.js, Neataptic, and Box2D
GeneticPromptLab uses genetic algorithms for automated prompt engineering (for LLMs), enhancing quality and diversity through iterative selection, crossover, and mutation, while efficiently exploring minimal yet diverse samples from the training set.
Neuroevolution framework for Python.
Applying Evolutionary Computing to Embeddings of Diffusion Models