Application of grammatical evolution to stock price prediction

  • Eisuke Kita Nagoya University
  • Hideyuki Sugiura Nagoya University
  • Yi Zuo Nagoya University
  • Takao Mizuno Nagoya University


Grammatical evolution (GE) is one of evolutionary computation techniques. The aim of GE is to find the function or the executable program or program fragment that will find the optimal solution for the design objective such as the function for representing the set of given data, the robot control algorithm and so on. Candidate solutions are described in bitstring. The mapping process from the genotype (bitstring) to the phenotype (function or program or program fragment) is defined according to the list of production rules of terminal and non-terminal symbols. Candidate solutions are evolved according to the search algorithm based on genetic algorithm (GA). There are three main issues in GE: genotype definition, production rules, and search algorithm. Grammatical evolution with multiple chromosomes (GEMC) is one of the improved algorithms of GE. In GEMC, the convergence property of GE is improved by modifying the genotype definition. The aim of this study is to improve convergence property by changing the search algorithm based on GA with the search algorithm based on stochastic schemata exploiter (SSE) in GE and GEMC. SSE is designed to find the optimal solution of the function, which is the same as GA. The convergence speed of SSE is much higher than that of GA. Moreover, the selection and crossover operators are not necessary for SSE. When GA is replaced with SSE, the improved algorithms of GE and GEMC are named “grammatical evolution by using stochastic schemata exploiter (GE-SSE)” and “grammatical evolution with multiple chromosome by using stochastic schemata exploiter (GEMC-SSE)”, respectively. In this study, GE-SSE is compared with GE in the symbolic regression problem of polynomial function. The results show that the convergence speed of GE-SSE is higher than that of original GE. Next, GE-SSE and GEMC-SSE are compared in stock price prediction problem. The results show that the convergence speed of GEMC-SSE is slightly higher than that of GE-SSE.


grammatical evolution (GE), mapping process, stochastic schemata exploiter (SSE), symbolic regression problem, stock price prediction,


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Sep 13, 2017
How to Cite
KITA, Eisuke et al. Application of grammatical evolution to stock price prediction. Computer Assisted Methods in Engineering and Science, [S.l.], v. 24, n. 1, p. 67-81, sep. 2017. ISSN 2299-3649. Available at: <>. Date accessed: 26 jan. 2022. doi: