The EMI Group has announced the official release of EvoX 1.0.0, a distributed GPU-accelerated evolutionary computation framework that now offers full compatibility with PyTorch. This major update transforms EvoX into a powerful, high-performance optimization tool for deep learning, reinforcement learning, and large-scale industrial applications.
🚀 What’s New in EvoX 1.0.0?
- 🔥 Full PyTorch Compatibility: EvoX now integrates seamlessly with the PyTorch ecosystem, making it easier than ever to apply evolutionary algorithms (EAs) in neural architecture search (NAS), reinforcement learning (RL), and meta-learning.
- ⚡ Distributed GPU Acceleration: Built for large-scale computation, EvoX leverages PyTorch for 100x speedup on heterogeneous hardware (CPUs, GPUs, multi-node clusters).
- 📦 Extensive Algorithm Library: Features 50+ evolutionary algorithms, including GA, DE, PSO, CMA-ES, MOEAs (NSGA-II, RVEA, MOEA/D, etc.), and state-of-the-art meta-evolution methods.
- 🎮 RL & Physics Engine Support: Compatible with Brax and reinforcement learning environments, enabling evolutionary reinforcement learning (ERL) applications.
- 📊 100+ Benchmark Problems: Covers single-objective and multi-objective optimization, as well as real-world engineering challenges.
- 🛠️ Customizable & Scalable: Supports flexible problem definitions, real-time data streaming, and scalable distributed workflows.
Bridging Evolutionary Computation and Deep Learning
EvoX 1.0.0 represents a groundbreaking step in merging evolutionary algorithms with modern deep learning frameworks. The integration with PyTorch enables researchers and practitioners to combine gradient-based learning with evolutionary search, unlocking new possibilities in AI-driven optimization, automated machine learning (AutoML), and complex decision-making systems.
Open-Source & Community-Driven
EvoX is now available on GitHub: https://github.com/EMI-Group/EvoX
💡 For updates and discussions, join the EvoX community on GitHub, Discord, and QQ Group (ID: 297969717).