Experimental costs are rising and it is important to use minimal resources to make statistical inference with maximal precision. Optimal design theory and ideas are increasingly applied to address design issues in a growing number of disciplines, and they include biomedicine, biochemistry, education, agronomy, manufacturing industry, toxicology and food science, to name a few.
I first present a brief overview of optimal design methodology and recent advances in the field. Nature-inspired meta-heuristic algorithms are then introduced to find optimal designs for potentially any model and any design criterion. This approach works quite magically and frequently finds the optimal solution or a nearly optimal solution for an optimization problem in a very short time. There is virtually no technical assumption required for the approach to perform well and the user only needs to input a few easy tuning parameters in the algorithm. Using popular models from the biopharmaceutical sciences as examples, I show how these algorithms find different types of optimal designs for dose response studies, including mini-max types of optimal designs where effective algorithms to find them have remained stubbornly elusive until now.
More information about Weng Kee Wong may be found at http://www.biostat.ucla.edu/Directory/Wwong