Using Genetic Programming for Data Science: Lessons Learned

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Gustafson:2015:GPTP,

• author = "Steven Gustafson and Ram Narasimhan and Ravi Palla and Aisha Yousuf",
• title = "Using Genetic Programming for Data Science: Lessons Learned",
• booktitle = "Genetic Programming Theory and Practice XIII",
• year = "2015",
• editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon",
• series = "Genetic and Evolutionary Computation",
• pages = "117--135",
• address = "Ann Arbor, USA",
• month = "14-16 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Data Science, Gradient boosted regression, Machine learning, Industrial applications, Real-world application, Lessons learned, Diversity, Ensembles",
• isbn13 = "978-3-319-34223-8",
• URL = "http://www.springer.com/us/book/9783319342214",
• DOI = "doi:10.1007/978-3-319-34223-8_7",
• abstract = "In this chapter we present a case study to demonstrate how the current state-of-the-art Genetic Programming (GP) fairs as a tool for the emerging field of Data Science. Data Science refers to the practice of extracting knowledge from data, often Big Data, to glean insights useful for predicting business, political or societal outcomes. Data Science tools are important to the practice as they allow Data Scientists to be productive and accurate. GP has many features that make it amenable as a tool for Data Science, but GP is not widely considered as a Data Science method as of yet. Thus, we performed a real-world comparison of GP with a popular Data Science method to understand its strengths and weaknesses. GP proved to find equally strong solutions, leveraged the new Big Data infrastructure, and was able to provide several benefits like direct feature importance and solution confidence. GP lacked the ability to quickly build and test models, required much more intensive computing power, and, due to its lack of commercial maturity, created some challenges for productization as well as integration with data management and visualization capabilities. The lessons learned leads to several recommendations that provide a path for future research to focus on key areas to improve GP as a Data Science tool.",
• notes = "

  Part of \cite{Riolo:2015:GPTP} published after the workshop in 2016",

}

Genetic Programming entries for Steven M Gustafson Ram Narasimhan Ravi Palla Aisha Yousuf

Citations