  The effective planning and allocation of resources in modern breeding
programs is a complex task. Breeding program design and operational management
have a major impact on the success of a breeding program and changing
parameters such as the number of selected/phenotyped/genotyped individuals will
impact genetic gain, genetic diversity, and costs. As a result, careful
assessment and balancing of design parameters is crucial, considering the
trade-offs between different breeding goals and associated costs. In a previous
study, we optimized the resource allocation strategy in a dairy cattle breeding
scheme via the combination of stochastic simulations and kernel regression,
aiming to maximize a target function containing genetic gain and the inbreeding
rate under a given budget. However, the high number of simulations required
when using the proposed kernel regression method to optimize a breeding program
with many parameters weakens the effectiveness of such a method. In this work,
we are proposing an optimization framework that builds on the concepts of
kernel regression but additionally makes use of an evolutionary algorithm to
allow for a more effective and general optimization. The key idea is to
consider a set of potential parameterizations of the breeding program, evaluate
their performance based on stochastic simulations, and use these outputs to
derive new parametrization to test in an iterative procedure. The evolutionary
algorithm was implemented in a Snakemake pipeline to allow for efficient
scaling on large distributed computing platforms. The algorithm achieved
convergence to the same optimum with a massively reduced number of simulations.
Thereby, the incorporation of class variables and accounting for a higher
number of parameters in the optimization pipeline leads to substantially
reduced computing time and better scaling for the desired optimization of a
breeding program.
