Maximizing quantitative traits in the mating design problem via simulation-based Pareto estimation. Hunter, S. R. & McClosky, B. Under Review, 2014. abstract bibtex Commercial plant breeders improve economically important traits by selectively mating individuals from a given breeding population. Potential pairings are evaluated before the growing season using Monte Carlo simulation, and a mating design is created to allocate a fixed breeding budget across the parent pairs to achieve desired population outcomes. We introduce a novel objective function for this so-called mating design problem that accurately models the goals of a certain class of breeding experiments. The resulting mating design problem is a computationally burdensome simulation optimization problem on a combinatorially large set of feasible points. To reduce the search space, we analytically identify a Pareto set of parent pairs that receives the entire breeding budget at optimality. We then estimate the Pareto set using a sequential implementation of a new asymptotically optimal simulation budget allocation. Finally, we use branch and bound to allocate the breeding budget within the estimated Pareto set. This procedure results in provably optimal mating designs, subject to estimation error. Our approach dramatically reduces the computational effort required to solve the mating design problem when compared to naive methods.
@misc{2014hunmcc,
Year = {2014},
Author = {S. R. Hunter and B. {McClosky}},
Title = {Maximizing quantitative traits in the mating design problem via simulation-based {P}areto estimation},
howpublished = {Under Review},
doi = {},
abstract = {Commercial plant breeders improve economically important traits by selectively mating individuals from a given breeding population. Potential pairings are evaluated before the growing season using Monte Carlo simulation, and a mating design is created to allocate a fixed breeding budget across the parent pairs to achieve desired population outcomes. We introduce a novel objective function for this so-called mating design problem that accurately models the goals of a certain class of breeding experiments. The resulting mating design problem is a computationally burdensome simulation optimization problem on a combinatorially large set of feasible points. To reduce the search space, we analytically identify a Pareto set of parent pairs that receives the entire breeding budget at optimality. We then estimate the Pareto set using a sequential implementation of a new asymptotically optimal simulation budget allocation. Finally, we use branch and bound to allocate the breeding budget within the estimated Pareto set. This procedure results in provably optimal mating designs, subject to estimation error. Our approach dramatically reduces the computational effort required to solve the mating design problem when compared to naive methods.}}
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