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Nature-Inspired Metaheuristic Algorithms for Generating Optimal Experimental Designs

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Design and Analysis of Experiments

We explore a particle swarm optimization (PSO) method for finding optimal experimental designs. This method is relatively new, simple yet powerful and widely used in many fields to tackle real problems. The method does not assume the objective function to be optimized is convex or differentiable. We demonstrate using examples that once a given regression model is specified, the PSO method can generate many types of optimal designs quickly, including optimal minimax designs where effective algorithms to generate such designs remain elusive.

This talk is part of the Isaac Newton Institute Seminar Series series.

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