| evojax | evosax | |
|---|---|---|
| 11 | 2 | |
| 786 | 450 | |
| 2.3% | - | |
| 4.6 | 6.7 | |
| 7 months ago | 9 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
evojax
Posts with mentions or reviews of evojax.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-26.
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[P] EvoTorch 0.4.0 dropped with GPU-accelerated implementations of CMA-ES, MAP-Elites and NSGA-II.
Awesome results! Would love to see a comparison with other accelerated evolution methods (eg https://github.com/google/evojax)
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Online courses on statistics and multi-objective optimization
For which problems do you want to apply multiple-objective optimization? What are the objectives? https://mml-book.github.io/ is only about single objective optimization (including constraints). Multi objective reinfocement learning (https://arxiv.org/pdf/1908.08342.pdf) is not really MO-optimization. Machine learning optimization frameworks like https://github.com/google/evojax support single objective optimization and quality-diversity (MAP-elites), but not MO-optimization.
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Question on how to model a "discontinuous" action space
population based algos dont care about differentiability https://github.com/google/evojax https://github.com/RobertTLange/evosax https://github.com/nnaisense/evotorch https://github.com/uber-research/PyTorch-NEAT
- Should I pursue Evolutionary Strategies?
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Performance of Evolutionary Algorithms for Machine Learning
Googles evojax project shows that evolutionary algorithms may be applied in the machine learning domain. And https://github.com/google/jax provides means to implement these algorithms to be deployed on CPUs/GPUs or even TPUs. But some questions remain unanswered:
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[D] What's your favorite unpopular/forgotten Machine Learning method?
Check out EvoJAX if you haven't seen it! Recently released for neuroevolution
- EvoJAX: Hardware-Accelerated Neuroevolution
- Show HN: EvoJAX: Hardware-Accelerated Neuroevolution
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[P] EvoJAX: Hardware-Accelerated Neuroevolution
Code for https://arxiv.org/abs/2202.05008 found: https://github.com/google/evojax
evosax
Posts with mentions or reviews of evosax.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-09.
-
Question on how to model a "discontinuous" action space
population based algos dont care about differentiability https://github.com/google/evojax https://github.com/RobertTLange/evosax https://github.com/nnaisense/evotorch https://github.com/uber-research/PyTorch-NEAT
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"Accelerated Quality-Diversity for Robotics through Massive Parallelism", Lim et al 2022 (MAP-Elites on TPU pods)
Even more implementations: https://github.com/RobertTLange/evosax
What are some alternatives?
When comparing evojax and evosax you can also consider the following projects:
evotorch - Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.
cuml - cuML - RAPIDS Machine Learning Library
PyTorch-NEAT
fast-cma-es - A Python 3 gradient-free optimization library