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Nature-inspired meta-heuristic algorithms for generating optimal experimental designs

Presented by: 
WK Wong University of California, Los Angeles
Date: 
Wednesday 8th July 2015 - 10:20 to 11:00
Venue: 
INI Seminar Room 1
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 deterministic state-of-the art algorithms.

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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons