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Application of grammatical evolution to stock price prediction

Kita E., Sugiura H., Zuo Y., Mizuno T.

Computer Assisted Methods in Engineering and Science

2017

Vol. 24, no. 1

67--81

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 bit string. 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 GEM Care 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.

A Reactive Prediction Based Dynamic Scheduling Method and its Performance Analysis

Zhao F., Zuo Y., Wang J.

Przegląd Elektrotechniczny

2013

R. 89, nr 5

176-181

The key problem of improving the efficiency of scheduling and the utilization of resource in manufacture system is the model of scheduling. However, the performance of the scheduling is disturbed by the uncertain elements in the system. This paper proposes a dynamic scheduling method which is based on reactive prediction to solve the interference. The mathematical statistics prediction theory is introduced in scheduling by using the mathematical statistics prediction model in the prediction scheduling. The time series data are obtained in the process of forecasting. The efficiency of distribution in the scheduling is improved by the forecasted data. There have three kinds of measurements consist of MSE, MAD and MAPE to analyze seven kinds of classics mathematical statistics prediction methods. The result shows that the dynamic scheduling method can eliminate the interference of the uncertain element in the scheduling.

W artykule przedstawiono metodę optymalizacji harmonogramu zadań w procesie produkcyjnym w celu zwiększenia sprawności i wykorzystania dostępnych środków. Proponowane rozwiązanie oparte jest na predykcji biernej (matematyka statystyczna), która pozwala na dynamiczne szeregowanie zadań. Przeprowadzone badania, wykazały, że zastosowane szeregowanie zadań zapobiega ich interferencji.