Title: Nature-inspired meta-heuristic algorithms for generating optimal experimental designs
Authors: Weng Kee Wong - UCLA (United States) [presenting]
Abstract: Nature-inspired meta-heuristic algorithms are increasingly studied and used in computer science and engineering 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 frequently able to find an optimal or a nearly optimal solution quickly. These general optimization methods usually do not require any assumption on the function to be optimized and the user only needs to input a few tuning parameters. It is given an overview of such algorithms, demonstrate the usefulness of some of these algorithms for finding different types of optimal designs for nonlinear models, suggest what to do if they don't seem to work and ascertain their overall potential.