ABSTRACT
We present a novel algorithm for the design of crossing experiments. A set of individuals (a “crossing-set”) is chosen from a larger set of potential crossing-sets by maximizing the distribution of a trait of interest. In simulated mating experiments, identified crossing-sets provide better estimates of underlying parameter values than randomly chosen crossing-sets.
Copyright
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