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Implementing the template method pattern in genetic programming for improved time series prediction

  • David Moskowitz
Article
  • 144 Downloads

Abstract

Modularity is an ongoing focus in genetic programming research. Enhanced modularity can accelerate solution convergence and increase human understanding and knowledge gained from evolved programs. Prior advances in modularity research have addressed programming language elements such as functions, modules, and recursion. This paper proposes improving modularity by considering non-language elements, specifically software design patterns. A new genetic programming technique implementing the template method pattern is described. This technique was tested and compared to existing genetic programming approaches in the prediction of nonlinear time series subject to abrupt changes in the underlying data generation process. Such series are often seen in areas such as finance and meteorology and have proved challenging for genetic programming to model and predict. Experimental results demonstrate the potential for incorporating additional software design patterns into genetic programming and applying these techniques to additional problem domains.

Keywords

Genetic programming Time series prediction Software design patterns Modularity 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Infoblazer, LLCStamfordUnited States
  2. 2.Nova Southeastern UniversityFort LauderdaleUnited States

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