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