Generate high-quality triangulated and polygonal art from images.
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Updated
Sep 2, 2021 - Go
Generate high-quality triangulated and polygonal art from images.
High-Performance Symbolic Regression in Python and Julia
Evolutionary algorithm toolbox and framework with high performance for Python
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Evolutionary multi-objective optimization platform
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.
🍀 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
A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
OptimLib: a lightweight C++ library of numerical optimization methods for nonlinear functions
🧬 Training the car to do self-parking using a genetic algorithm
Automated modeling and machine learning framework FEDOT
Distributed High-Performance Symbolic Regression in Julia
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
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).
EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
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