Abstract

We present a case study of the performance testing of a commercially engineered genetic programming algorithm applied to the automated modeling of industrial machine learning problems. This paper summarizes some of what has been learned over the past five years of working with a large number of industrial machine learning challenges in a commercial or enterprise setting. Automation and parallelism via cloud computing is used to reduce test time. Two frameworks for conducting performance tests are discussed, highlighting the advantages of collecting statistics throughout the search. A performance test suite of industrial machine learning problems is described, and examples of performance test results are shown. Finally, a summary of challenges and open questions is provided.

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