Benchmarking hyperparameter optimization strategies for crop genomic prediction

Genomic prediction has become an important approach for accelerating crop breeding by using genome-wide marker information to predict complex traits. However, the performance of genomic prediction models is influenced not only by model selection but also by hyperparameter optimization strategies, which have received limited attention in crop genomic prediction studies. Based on four crop datasets, including maize, rice, wheat, and foxtail millet, we evaluated the prediction performance and computational efficiency of six representative genomic prediction models under four hyperparameter optimization strategies. The results showed that the optimal optimization strategy was jointly affected by crop species, target traits, and model types, and no single strategy consistently performed best across all scenarios. Bayesian optimization showed good prediction stability and achieved high prediction accuracy for most traits and models, especially in the rice and wheat datasets. Grid search and random search also showed strong competitiveness in some datasets. In contrast, although halving search reduced computational cost, it showed lower prediction stability in some cases. Our study highlights the important role of hyperparameter optimization in genomic prediction and provides a useful reference for selecting efficient optimization strategies in crop breeding applications.