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Automated Design of Genetic Programming Classification Algorithms for Financial Forecasting Using Evolutionary Algorithms

  • Thambo Nyathi
  • Nelishia Pillay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

In this work two metaheuristic algorithms namely, a genetic algorithm (GA) and grammatical evolution (GE) are used to configure genetic programming classification algorithms for financial forecasting. The performance of the classifiers evolved through a GA and GE design are compared to the performance of classifiers evolved using the traditional manual design approach. Fifteen stocks from varied sectors are selected to evaluate the performance. Additionally, the fitness landscape of the design space evolved by grammatical evolution and the genetic algorithm is evaluated. Results demonstrate that GE designed algorithms evolve classifiers that perform better than those designed by a GA and manually designed. Furthermore, it is established that the GA design space is more rugged than the GE design space.

Keywords

Genetic programming Grammatical evolution Genetic algorithms Financial forecasting Classification 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Mathematics, Statistics and Computer ScienceUniversity of KwaZulu-NatalPietermaritzburgSouth Africa
  2. 2.Department of Computer ScienceUniversity of PretoriaPretoriaSouth Africa

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