nnaisense/evotorch

Health v2 · maintenance only

Adoption not part of the score

1142 stars · 79 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EvoTorch is an open-source evolutionary computation library built on top of PyTorch, developed at NNAISENSE. It provides distribution-based (PGPE, XNES, CMA-ES, SNES, CEM) and population-based (GeneticAlgorithm/NSGA-II, CoSyNE, MAPElites) search algorithms for black-box optimization, reinforcement learning, and supervised learning tasks.

Use cases

• solve black-box optimization problems without gradients
• train neural network policies via neuroevolution
• run CMA-ES or PGPE on GPU with PyTorch
• perform multi-objective optimization with NSGA-II
• apply quality-diversity search with MAPElites
• evolve solutions to reinforcement learning tasks

When to choose

• your objective is non-differentiable, noisy, or black-box
• you want evolutionary algorithms accelerated on GPU via PyTorch
• you need distributed or parallelized population-based search
• you want neuroevolution for RL or supervised learning

When to avoid

• your problem is smooth and gradient descent works well
• you need a lightweight non-PyTorch optimizer
• you only need simple hyperparameter tuning (use optuna instead)

Facets

library · maturity active

machine-learning gpu-computing reinforcement-learning artificial-intelligence machine-learning reinforcement-learning python cross-platform evolutionary-computation neuroevolution pytorch genetic-algorithm cma-es black-box-optimization quality-diversity optimization algorithms gpu

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