# Optimal Experimental Design of Field Trials using Differential Evolution

## TL;DR

This paper introduces a differential evolution-based method for optimizing the design of field trials, improving genotype placement to reduce bias and enhance experimental efficiency.

## Contribution

It proposes a novel model-based optimization approach using differential evolution for field trial design, addressing genotype placement considering kinship effects.

## Key findings

- The method achieves better convergence compared to existing strategies.
- It provides optimal strategies for genotype placement within and between fields.
- Results show improved exploration of the search space.

## Abstract

When setting up field experiments, to test and compare a range of genotypes (e.g. maize hybrids), it is important to account for any possible field effect that may otherwise bias performance estimates of genotypes. To do so, we propose a model-based method aimed at optimizing the allocation of the tested genotypes and checks between fields and placement within field, according to their kinship. This task can be formulated as a combinatorial permutation-based problem. We used Differential Evolution concept to solve this problem. We then present results of optimal strategies for between-field and within-field placements of genotypes and compare them to existing optimization strategies, both in terms of convergence time and result quality. The new algorithm gives promising results in terms of convergence and search space exploration.