srbench
yggdrasil-decision-forests
| srbench | yggdrasil-decision-forests | |
|---|---|---|
| 2 | 4 | |
| 194 | 429 | |
| 3.1% | 3.3% | |
| 9.1 | 9.5 | |
| 3 months ago | 6 days ago | |
| Python | C++ | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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srbench
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Ask HN: Is genetic programming still actively researched?
NEAT and neuroevolution in general are interesting approaches. I also suggest to check techniques like DENSER [1] that can be used to evolve deep networks (by using the evolutionary part on the network structure and not on the weights).
Genetic Programming (GP), however, has not evolved to NEAT (which itself is not very recent, being published in 2002) but simply neuroevolution has become one of the topics that are part of evolutionary computation (EC). For example, one of the largest yearly conferences on evolutionary computation (GECCO) [2] was just last month with both neuroevolution and GP tracks. It is however true that the success of neural techniques had an effect on the community, some effects are the discussion of the role of EC and, for example, more space given to hybrid works (see, for example, the joint track on evolutionary machine learning [3] inside the evostar event).
Related to the original post, a place where some recent research on GP can be found are the proceedings of GECCO (GP track), EuroGP (part of evostar), PPSN (Parallel Problem Solving from Nature), and IEEE CEC (IEEE Congress on Evolutionary Computation) and journals like Genetic Programming and Evolvable Machine (GPEM), Swarm and Evolutionary Computation (SWEVO), and IEEE Transactions on Evolutionary Computation (IEEE TEVC). The list is not exhaustive, but those are some well-known venues.
For a less "daunting" starting point, some recent techniques are being added to the SRBench benchmark suite [4], with links to both the code and the paper describing the technique.
[1] Assunção, F., Lourenço, N., Machado, P., & Ribeiro, B. (2019, March). Fast denser: Efficient deep neuroevolution. In european conference on genetic programming (pp. 197-212). Cham: Springer International Publishing.
[2] https://gecco-2023.sigevo.org/HomePage
[3] https://www.evostar.org/2024/eml/
[4] https://github.com/cavalab/srbench
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Why do tree-based models still outperform deep learning on tabular data?
A great paper and an important result.
However, it omits to cite the highly relevant SRBench paper from 2021, which also carefully curates a suitable set of regression benchmarks and shows that Genetic Programming approaches also tend to be better than deep learning.
https://github.com/cavalab/srbench
cc u/optimalsolver
yggdrasil-decision-forests
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Why do tree-based models still outperform deep learning on tabular data? (2022)
Is it this library https://github.com/google/yggdrasil-decision-forests ?
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Binary image classification using random forest algorithm
However if you know cpp you can use Yggdrasil https://github.com/google/yggdrasil-decision-forests.
- Why do tree-based models still outperform deep learning on tabular data?
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[P] Tree compiler that speeds up LightGBM model inference by ~30x
Have you tried to compare with Yggdrasil, the decision forest engine (c++, both training and inference) powering TensorFlow Decision Forests ?
What are some alternatives?
Spearmint - Spearmint Bayesian optimization codebase
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
decision-forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
tensorflow - An Open Source Machine Learning Framework for Everyone
decision-tree-classifier - Decision Tree Classifier and Boosted Random Forest
flashlight - A C++ standalone library for machine learning [Moved to: https://github.com/flashlight/flashlight]
interpret - Fit interpretable models. Explain blackbox machine learning.
higgs-logistic-regression
MLBenchmarks.jl - ML models benchmarks on public dataset