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

ABSTRACT:

Motivation

Differentiable programming frameworks like PyTorch and JAX revolutionized biological modeling. A foremost merit is that multiple components programmed separately can be put together so that the parameters are jointly optimized. Despite its proven value in agricultural applications, existing breeding simulators are non-differentiable, hindering integration into general deep learning systems.

Results

In this paper, we present DiffBreed, a differentiable breeding simulator. Its performance was evaluated in gradient-based optimization of a progeny allocation strategy that maximizes the genetic gain. By utilizing gradient-based optimization, DiffBreed refined progeny allocation strategies, achieving superior genetic gains compared to a non-optimized equal allocation ap

SUBMITTER: Hamazaki K 

PROVIDER: S-EPMC12620001 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature