How Competitive Is Genetic Programming in Business Data Science Applications?
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Kordon:2019:GPTP,
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author = "Arthur Kordon and Theresa Kotanchek and
Mark Kotanchek",
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title = "How Competitive Is Genetic Programming in Business
Data Science Applications?",
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booktitle = "Genetic Programming Theory and Practice XVII",
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year = "2019",
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editor = "Wolfgang Banzhaf and Erik Goodman and
Leigh Sheneman and Leonardo Trujillo and Bill Worzel",
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pages = "145--163",
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address = "East Lansing, MI, USA",
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month = "16-19 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Business applications, Data science,
Competitive advantage, Research marketing",
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isbn13 = "978-3-030-39957-3",
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DOI = "doi:10.1007/978-3-030-39958-0_8",
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abstract = "The paper evaluates GP competitiveness in business
data science-driven applications and suggests the
necessary steps to increase its reach, impact and
competitiveness. First, the key business needs for Data
Science are identified and discussed, followed by an
analysis of the competitive landscape and popularity of
Data Science methods. The competitive advantages and
weaknesses of GP as well its impressive application
record are reviewed. Two business applications with
high value creation (inferential sensors and nonlinear
business forecasting) are identified and described. The
recommended action items to increase competitive
presence of GP in Data Science business applications
include: develop a successful marketing strategy toward
statistical, machine/deep learning, and business
communities; broaden application areas; improve
professional development tools; and increase GP
visibility and teaching in Data Science classes.",
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notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the
workshop",
- }
Genetic Programming entries for
Arthur K Kordon
Theresa Kotanchek
Mark Kotanchek
Citations