Abstract
The paper evaluates GP’s 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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Kordon, A., Kotanchek, T., Kotanchek, M. (2020). How Competitive Is Genetic Programming in Business Data Science Applications?. In: Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (eds) Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-39958-0_8
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