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

Duration: 45 mins 44 secs

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


Created:	2015-07-13 12:53
Collection:	Design and Analysis of Experiments
Publisher:	Isaac Newton Institute
Copyright:	Wong, WK
Language:	eng (English)


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.

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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