Wyniki wyszukiwania - Biblioteka Nauki

1

Diagnosing corporate stability using grammatical evolution

100%

Brabazon A. , O'Neill M.

International Journal of Applied Mathematics and Computer Science

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2004

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tom 14

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nr 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.

2

Exploring random permutations effects on the mapping process for grammatical evolution

80%

Zúñiga B. V. , Carpio J. M. , Sotelo-Figueroa M. A. , Espinal A. , Purata-Sifuentes O. J. , Ornelas M. , Soria-Alcaraz J. A. , Rojas A.

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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tom Vol. 14, No. 1

65--72

EN

Grammatical Evolution (GE) is a form of Genetic Programming (GP) based on Context-Free Grammar (CF Grammar). Due to the use of grammars, GE is capable of creating syntactically correct solutions. GE uses a genotype encoding and is necessary to apply a Mapping Process (MP) to obtain the phenotype representation. There exist some well-known MPs in the state-of-art like Breadth-First (BF), Depth-First (DF), among others. These MPs select the codons from the genotype in a sequential manner to do the mapping. The present work proposes a variation in the selection order for genotype’s codons; to achieve that, it is applied a random permutation for the genotype’s codons order-taking in the mapping. The proposal’s results were compared using a statistical test with the results obtained by the traditional BF and DF using the Symbolic Regression Problem (SRP) as a benchmark.

3

Diagnosing corporate stability using grammatical evolution

60%

Brabazon A. , O'Neill M.

International Journal of Applied Mathematics and Computer Science

|

2004

|

tom 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.

4

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

51%

Kuroda T. , Sugiura H. , Kita E.

Computer Assisted Methods in Engineering and Science

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2013

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tom 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.