Performance Testing of Automated Modeling for Industrial Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Sherry:2017:GECCO,

• author = "Dylan Sherry and Michael Schmidt",
• title = "Performance Testing of Automated Modeling for Industrial Applications",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "1605--1612",
• size = "8 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3082534",
• DOI = "doi:10.1145/3067695.3082534",
• acmid = "3082534",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, benchmark, case study, machine learning, performance test",
• month = "15-19 " # jul,
• abstract = "We present a case study of the performance testing of a commercially engineered genetic programming algorithm applied to the automated modelling 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.",
• notes = "Also known as \cite{Sherry:2017:PTA:3067695.3082534} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Dylan Sherry Michael D Schmidt

Citations