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SUMMARY:Nature-inspired meta-heuristic algorithms for generating optimal e
 xperimental designs - Wong\, WK (University of California\, Los Angeles)
DTSTART:20150708T092000Z
DTEND:20150708T100000Z
UID:TALK60076@talks.cam.ac.uk
CONTACT:42080
DESCRIPTION:Nature-inspired meta-heuristic algorithms are increasingly stu
 died and used in many disciplines to solve high-dimensional complex optimi
 zation problems in the real world. It appears relatively few of these algo
 rithms are used in mainstream statistics even though they are simple to im
 plement\, very flexible and able to find an optimal or a nearly optimal so
 lution quickly. Frequently\, these methods do not require any assumption o
 n the function to be optimized and the user only needs to input a few tuni
 ng parameters. \n\nI will demonstrate the usefulness of some of these algo
 rithms for finding different types of optimal designs for nonlinear models
  in dose response studies. Algorithms that I plan to discuss are more rece
 nt ones such as Cuckoo and Particle Swarm Optimization.  I also \ncompare 
 their performances and advantages relative to deterministic state-of-the a
 rt algorithms.\n
LOCATION:Seminar Room 1\, Newton Institute
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