A simulation framework for reciprocal recurrent selection-based hybrid breeding under transparent and opaque simulators

Hybrid breeding is an established and effective process to improve offspring performance, while it is resource-intensive and time-consuming for the recurrent process in reality. To enable breeders and researchers to evaluate the effectiveness of competing decision-making strategies, we present a modular simulation framework for reciprocal recurrent selection-based hybrid breeding. Consisting of multiple modules such as heterotic separation, genomic prediction, and genomic selection, this simulation framework allows breeders to efficiently simulate the hybrid breeding process with multiple options of simulators and decision-making strategies. We also integrate the recently proposed concepts of transparent and opaque simulators into the framework in order to reflect the breeding process more realistically. Simulation results show the performance comparison among different breeding strategies under the two simulators

Transposed Poisson structures on Galilean and solvable Lie algebras

Transposed Poisson structures on complex Galilean type Lie algebras and superalgebras are described. It was proven that all principal Galilean Lie algebras do not have non-trivial $\frac{1}{2}$-derivations and as it follows they do not admit non-trivial transposed Poisson structures. Also, we proved that each complex finite-dimensional solvable Lie algebra admits a non-trivial transposed Poisson structure and a non-trivial ${\rm Hom}$-Lie structure