neat (wip)
Explorations into NEAT and some of its derivative research
install
In project root, run
$ sh install.sh
quick test
$ uv run train_lunar.py
citations
@article{Stanley2011CompetitiveCT,
title = {Competitive Coevolution through Evolutionary Complexification},
author = {Kenneth O. Stanley and Risto Miikkulainen},
journal = {ArXiv},
year = {2011},
volume = {abs/1107.0037},
url = {https://api.semanticscholar.org/CorpusID:11881625}
}
@inproceedings{4665912,
author = {Miguel, Cesar Gomes and Silva, Carolina Feher da and Netto, Marcio Lobo},
booktitle = {2008 10th Brazilian Symposium on Neural Networks},
title = {Structural and Parametric Evolution of Continuous-Time Recurrent Neural Networks},
year = {2008},
doi = {10.1109/SBRN.2008.12}
}
@article{Khamesian2021HybridSN,
title = {Hybrid self-attention NEAT: a novel evolutionary self-attention approach to improve the NEAT algorithm in high dimensional inputs},
author = {Saman Khamesian and Hamed Malek},
journal = {Evolving Systems},
year = {2021},
pages = {1-15},
url = {https://api.semanticscholar.org/CorpusID:244920723}
}
@article{Hornby2006AutomatedAD,
title = {Automated Antenna Design with Evolutionary Algorithms},
author = {Gregory Hornby and Al Globus and Derek S. Linden and Jason D. Lohn},
journal = {Space},
year = {2006},
url = {https://api.semanticscholar.org/CorpusID:8290212}
}
@inproceedings{schrum:gecco14,
title = {Evolving Multimodal Behavior With Modular Neural Networks in Ms. Pac-Man},
author = {Jacob Schrum and Risto Miikkulainen},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014)},
month = {July},
address = {Vancouver, BC, Canada},
pages = {325--332},
note = {Best Paper: Digital Entertainment and Arts},
url = {http://www.cs.utexas.edu/users/ai-lab?schrum:gecco2014},
year = {2014}
@article{stanley:ec02,
title = {Evolving Neural Networks Through Augmenting Topologies},
author = {Kenneth O. Stanley and Risto Miikkulainen},
volume = {10},
journal = {Evolutionary Computation},
number = {2},
pages = {99-127},
url = "http://nn.cs.utexas.edu/?stanley:ec02",
year = {2002}
}
@misc{doerr2017fastgeneticalgorithms,
title = {Fast Genetic Algorithms},
author = {Benjamin Doerr and Huu Phuoc Le and Régis Makhmara and Ta Duy Nguyen},
year = {2017},
eprint = {1703.03334},
archivePrefix = {arXiv},
primaryClass = {cs.NE},
url = {https://arxiv.org/abs/1703.03334},
}
@misc{legg2004tournamentversusfitnessuniform,
title = {Tournament versus Fitness Uniform Selection},
author = {Shane Legg and Marcus Hutter and Akshat Kumar},
year = {2004},
eprint = {cs/0403038},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/cs/0403038},
}
@article{hiraga2024improving,
title = {Improving the performance of mutation-based evolving artificial neural networks with self-adaptive mutations},
author = {Hiraga, Motoaki and Komura, Masahiro and Miyamoto, Akiharu and Morimoto, Daichi and Ohkura, Kazuhiro},
journal = {PLOS ONE},
volume = {19},
number = {7},
pages = {e0307084},
year = {2024},
publisher = {Public Library of Science},
doi = {10.1371/journal.pone.0307084},
url = {https://doi.org/10.1371/journal.pone.0307084}
}
@inproceedings{tackett1994unique,
title = {The unique implications of brood selection for genetic programming},
author = {Tackett, Walter Alden and Carmi, Aviram},
booktitle = {Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence},
pages = {160--165},
year = {1994},
organization = {IEEE},
doi = {10.1109/ICEC.1994.350030}
}
Metadata
Release files for x-neat 0.0.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| x_neat-0.0.5.tar.gz | 23.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| x_neat-0.0.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 43.2 kB
Release files / x_neat-0.0.5.tar.gz
| Download URL | x_neat-0.0.5.tar.gz |
|---|---|
| Size | 23.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / x_neat-0.0.5-py3-none-any.whl
| Download URL | x_neat-0.0.5-py3-none-any.whl |
|---|---|
| Size | 19.4 kB |
| Tags | Python 3 |
|
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