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

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

Released Monday, 13th July 2015
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Nature-inspired meta-heuristic algorithms for generating optimal experimental designs

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

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

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

Monday, 13th July 2015
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Wong, WK (University of California, Los Angeles)Wednesday 8th July 2015, 10:20 - 11:00

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Design and Analysis of Experiments

Design of experiments was born as a result of an unlikely, but true anecdote: a lady claimed before R.A. Fisher that she was able to ascertain whether milk was poured before or after tea in her cuppa. Fisher devised a study to verify her claim and, in turn, this gave birth to Experimental Design. Agricultural experiments formed the core around which the theory evolved in its origins in the 1930s. The theory of Design of Experiments has since blossomed into many different approaches, ranging from optimal designs for dynamical models in pharmacokynetic studies, and designs for industrial experimentation, to designs of simulation experiments in climate change, to name but a few. The mathematical techniques currently used in different branches of Design are vast, for example Galois theory, non linear optimization, algebraic geometry and association schemes.This media collection contains seminars from the following INI programmes:Design and Analysis of Experiments 2011: http://www.newton.ac.uk/programmes/DAE/Design of Experiments 2008: http://www.newton.ac.uk/programmes/DOE/

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