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Abstract:
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A genetic algorithm (GA) is an evolutionary search strategy used to find an acceptable solution to a problem based on the optimization of a fitness function. A successful GA will 'evolve' more fit solutions until an acceptable solution has been generated. This roundtable discussion will involve a brief review of existing applications of GAs to the generation of experimental designs (e.g., optimal designs). This will be followed by a discussion of the potential of GAs to address other existing experimental design problems (e.g., constrained design regions, mixture designs, restrictions on randomization). Participants are encouraged to share their experiences with GAs, as well as any design generation problems they are encountering for which GAs may be applied.
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