DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies

The proposed automatic differentiation framework was implemented as the Python module "DiffBreed." This module and all the scripts used in this study, including the gradient-based optimization, are available from the "KosukeHamazaki/GORA-PT" repository on GitHub, https://github.com/KosukeHamazaki/GORA-PT. While the simulated datasets in the present study are available from the same repository, the optimized results by PyTorch were not shared in this repository due to the file sizes. Instead, all datasets, including the optimized ones, will be shared in the repository on Zenodo, https://doi.org/10.5281/zenodo.14046522.

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Publication Details

Journal
Bioinformatics
Published
2025-10-22
DOI
https://doi.org/10.1093/bioinformatics/btaf586
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies

Kosuke Hamazaki, Koji Tsuda, Hiroyoshi Iwata
Bioinformatics
Evolutionary Algorithms and Applications
article

DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies

Kosuke Hamazaki, Koji Tsuda, Hiroyoshi Iwata
article en

Abstract

The proposed automatic differentiation framework was implemented as the Python module "DiffBreed." This module and all the scripts used in this study, including the gradient-based optimization, are available from the "KosukeHamazaki/GORA-PT" repository on GitHub, https://github.com/KosukeHamazaki/GORA-PT. While the simulated datasets in the present study are available from the same repository, the optimized results by PyTorch were not shared in this repository due to the file sizes. Instead, all datasets, including the optimized ones, will be shared in the repository on Zenodo, https://doi.org/10.5281/zenodo.14046522.

BioinformaticsVol. 41(11)
RIKEN Center for Advanced Intelligence Project (JP), The University of Tokyo (JP)
ACT-X
Openalex Percentile: Top 84%
Evolutionary Algorithms and Applications
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