ant-research/AntOmniEvo
An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.
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Star counts measured between 00:41 and 02:33 (BKK).
An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.
Symbolic Learning in Python and Julia
Open-source implementation of AlphaEvolve
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
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).
Distributed High-Performance Symbolic Regression in Julia
A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
A fast and flexible evolution engine for implementing artificial evolution and genetic programming techniques
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
EvoMO is a GPU-accelerated library for evolutionary multiobjective optimization (EMO)
Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Gradient descent, but the parameters are agents — a parallel, asynchronous framework for self-evolving agents (skills, prompts, harnesses). Diffs are the gradients; the aggregator is the optimizer.
That's all 15 repositories in this period.