Optimization of breeding program design through stochastic simulation with evolutionary algorithms

TL;DR

This paper introduces an advanced optimization framework combining stochastic simulation, kernel regression, and evolutionary algorithms to efficiently design breeding programs, reducing computational costs while improving optimization accuracy.

Contribution

The study presents a novel integration of evolutionary algorithms with kernel regression-based simulation optimization for breeding program design, enhancing efficiency and scalability.

Findings

Achieved convergence to optimal breeding parameters with fewer simulations.

Reduced computational time significantly compared to previous methods.

Enhanced scalability for complex breeding program optimization.

Abstract

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.