Genomic selection has revolutionized breeding by enabling the early identification of superior individuals using genome-wide markers, enhancing breeding efficiency and accelerating variety development. Over the past decade, new selection and mating strategies--leveraging optimization methods and other approaches--have been introduced to improve various decision-making processes in breeding programs. However, optimizing breeding remains challenging when the positions and effects of quantitative trait loci are unknown. We developed a framework that optimizes breeding strategies while updating genomic prediction models during breeding schemes. By implementing intermediate model updates, we enabled re-optimization of allocation strategies based on updated predictions. Our simulations compared this approach with equal allocation and optimal cross selection methods across various selection intensities and genetic architectures. Results demonstrated our optimized allocation strategy significantly outperformed the other approaches under moderate to low selection intensities, particularly when combined with model updates. While genetic gains plateaued without updates, our approach enabled continuous improvement through the final generation. The framework showed exceptional robustness across different simulation conditions and better maintained genetic diversity while controlling changes in population structure. This confirms that optimized allocation strategies remain effective when using estimated marker effects rather than true effects, providing a practical framework for improving real-world breeding programs.
Hamazaki et al. (Sat,) studied this question.