Next Article in Journal
Parallel Reservoir Simulation with OpenACC and Domain Decomposition
Previous Article in Journal
A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(12), 212;

Evaluating Algorithm Efficiency for Optimizing Experimental Designs with Correlated Data

Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160, USA
School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA
Author to whom correspondence should be addressed.
Received: 16 November 2018 / Revised: 5 December 2018 / Accepted: 12 December 2018 / Published: 18 December 2018
Full-Text   |   PDF [1197 KB, uploaded 18 December 2018]   |  


The search for efficient methods and procedures to optimize experimental designs is a vital process in field trials that is often challenged by computational bottlenecks. Most existing methods ignore the presence of some form of correlations in the data to simplify the optimization process at the design stage. This study explores several algorithms for improving field experimental designs using a linear mixed models statistical framework adjusting for both spatial and genetic correlations based on A- and D-optimality criteria. Relative design efficiencies are estimated for an array of algorithms including pairwise swap, genetic neighborhood, and simulated annealing and evaluated with varying levels of heritabilities, spatial and genetic correlations. Initial randomized complete block designs were generated using a stochastic procedure and can also be imported directly from other design software. Results showed that at a spatial correlation of 0.6 and a heritability of 0.3, under the A-optimality criterion, both simulated annealing and simple pairwise algorithms achieved the highest design efficiencies of 7.4 % among genetically unrelated individuals, implying a reduction in average variance of the random treatment effects by 7.4 % when the algorithm was iterated 5000 times. In contrast, results under D-optimality criterion indicated that simulated annealing had the lowest design efficiency. The simple pairwise algorithm consistently maintained highest design efficiencies in all evaluated conditions. Design efficiencies for experiments with full-sib families decreased with increasing heritability. The number of successful swaps appeared to decrease with increasing heritability and were highest for both simulated annealing and simple pairwise algorithms, and lowest for genetic neighborhood algorithm. View Full-Text
Keywords: genetic relationships; greedy algorithms; pairwise swap; simulated annealing; spatial correlations genetic relationships; greedy algorithms; pairwise swap; simulated annealing; spatial correlations

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Mramba, L.K.; Gezan, S.A. Evaluating Algorithm Efficiency for Optimizing Experimental Designs with Correlated Data. Algorithms 2018, 11, 212.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top