Wyniki wyszukiwania - BazTech

1

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

EN

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.

2

Application of grammatical evolution to coefficient identification problem in two-dimensional elastic problem

Kuroda T., Sugiura H., Kita E.

Computer Assisted Methods in Engineering and Science

|

2013

|

Vol. 20, no. 1

3--13

EN

Grammatical evolution (GE), which is a kind of evolutionary algorithms, is designed to find a function, an executable program or program fragment that will achieve a good fitness value for the given objective function to be minimized. In this study, GE is applied for the coefficient identification problem of the stiffness matrix in the two-dimensional elastic problem. Finite element analysis of the plate with a circular hole is performed for determining the set of the stress and the strain components. Grammatical evolution determines the coefficient matrix of the relationship between the stress and strain components. The coefficient matrix is compared with Hooke's law in order to confirm the validity of the algorithm. After that, three algorithms are shown for improving the convergence speed of the original GE algorithm.

3

Diagnosing corporate stability using grammatical evolution

Brabazon A., O'Neill M.

International Journal of Applied Mathematics and Computer Science

|

2004

|

Vol. 14, no 3

363-374

EN

Grammatical Evolution (GE) is a novel data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and demonstrates the methodology by applying it to construct a series of models for the prediction of bankruptcy, employing information drawn from financial statements. Unlike prior studies in this domain, the raw financial information is not preprocessed into pre-determined financial ratios. Instead, the ratios to be incorporated into the classification rule are evolved from the raw financial data. This allows the creation and subsequent evolution of alternative ratio-based representations of the financial data. A sample of 178 publicly quoted, US firms, drawn from the period 1991 to 2000 are used to train and test the model. The best evolved model correctly classified 86 (77)% of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure.