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Justin Ng, Aaron Berliner, Joe Lachoff, Florencio Mazzoldi, Eli Groban
International Joint Conference on Artificial Intelligence
We propose a strategy to effectively search the large solution space for a genetic system built from combinations of genetic parts. An iterative and interchangeable algorithm then searches the combinatorial space and creates a reasonably sized test set to build using new highly parallel genetic foundry capabilities. The algorithm can also learn from information gained from prior iterations to suggest combinations to try in the following iteration.