Weng Kee Wong
Department of Biostatistics, University of California – Los Angeles

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 main stream 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 easy-to-use tuning parameters. In this talk, I provide an overview of such algorithms and demonstrate the usefulness of one of these algorithms for finding different types of optimal designs for nonlinear models. In particular, I will discuss use of particle swarm optimization techniques to design a dose response study as an illustrative application in the Bioscience.