Nature-Inspired Meta-Heuristic Algorithms for Generating Optimal Experimental Designs

• Activity Number: 165
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Weng Kee Wong* and Guanghao Qi
• Companies: UCLA and Fudan University
• Keywords: approximate design ; exact design ; equivalence theorem ; information matrix ; multiple-objective optimal design

Abstract

Nature-inspired meta-heuristic algorithms are increasingly studied and used in many disciplines to solve high-dimensional complex optimization problems in the real world. It appears relatively few of these algorithms are used in mainstream statistics even though they are simple to implement, very flexible and able to find an optimal or a nearly optimal solution quickly. Frequently, these methods do not require any assumption on the function to be optimized and the user only needs to input a few tuning parameters.

I will demonstrate the usefulness of some of these algorithms for finding different types of optimal designs for nonlinear models in dose response studies. Algorithms that I plan to discuss are more recent ones such as Cuckoo and Particle Swarm Optimization. I also compare their performances and advantages relative to a deterministic state-of-the art algorithm.