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DTSTART:19700329T010000
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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Nature-inspired meta-heuristic algorithms for gene
rating optimal experimental designs - Wong\, WK (U
niversity of California\, Los Angeles)
DTSTART;TZID=Europe/London:20150708T102000
DTEND;TZID=Europe/London:20150708T110000
UID:TALK60076AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/60076
DESCRIPTION:Nature-inspired meta-heuristic algorithms are incr
easingly studied and used in many disciplines to s
olve high-dimensional complex optimization problem
s in the real world. It appears relatively few of
these algorithms are used in mainstream statistics
even though they are simple to implement\, very f
lexible and able to find an optimal or a nearly op
timal solution quickly. Frequently\, these methods
do not require any assumption on the function to
be optimized and the user only needs to input a fe
w tuning parameters. \n\nI will demonstrate the us
efulness of some of these algorithms for finding d
ifferent types of optimal designs for nonlinear mo
dels in dose response studies. Algorithms that I p
lan to discuss are more recent ones such as Cuckoo
and Particle Swarm Optimization. I also \ncompar
e their performances and advantages relative to de
terministic state-of-the art algorithms.\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:
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