Mate selection by groups.

The two key components in a breeding program can be described as the development of an objective function and the implementation of an algorithm for mate selection to target the objective function. However, mate selection is unacceptably slow for objective functions that involve longer term aims, such as engineering a good connection between herds. This paper proposes animal clustering to facilitate group mate selection, plus a step to refine mate selection for individual animals. Cluster analysis is used to form groups of animals within each sex based on nominated factors such as breed, herd, age, and EBV. Mate selection is then performed on groups, reducing the size of the problem considerably. The example used involved a three-breed crossbred population with a simple objective function to maximize predicted progeny merit. Twenty sires and 500 dams were to be selected from 816 and 1784 candidates. Group mate selection achieved 96.9% efficiency (random selection, 0%; full mate selection, 100%) and took 0.28 s to compute (394.2 s for full mate selection). Permitting more candidates to reach the individual mate selection stage increased efficiency to, for example, 98.9% at the cost of an extra 1.15 s. Efficiency was also improved by permitting more clusters.