Wyniki wyszukiwania - BazTech

1

Exploring random permutations effects on the mapping process for grammatical evolution

Zúñiga Blanca Verónica, Carpio Juan Martín, Sotelo-Figueroa Marco Aurelio, Espinal Andrés, Purata-Sifuentes Omar Jair, Ornelas Manuel, Soria-Alcaraz Jorge Alberto, Rojas Alfonso

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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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.

2

Application of grammatical evolution to stock price prediction

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

Computer Assisted Methods in Engineering and Science

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2017

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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.

3

Modelowanie cyklu produkcyjnego przy użyciu techniki mapowania procesu

Topolska J.

Logistyka

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2014

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nr 6

PL

W przedsiębiorstwach reorganizacja procesu nie jest łatwym zadaniem, gdyż wymusza na osobach ją przeprowadzających spojrzenie na cały proces pod różnym kątem, z każdego możliwego punktu widzenia. Powoduje przez to ciągłe poszukiwanie nowych rozwiązań, które pozwoliłby osiągnąć zamierzony rezultat. W badaniach podjęto próbę przeorganizowania procesu produkcyjnego wykorzystując dane z przedsiębiorstwa, mapowanie całego procesu z uwzględnieniem wymagań klientów. Przeanalizowano rozbudowę stanowisk, dodatkowe zatrudnienie pod potrzeby zmiany cyklu pracy na linii produkcyjnej. Zmiany przedstawiono w postaci proponowanego stany w procesie mapowania. Reorganizacja procesu produkcyjnego pozwoli na utrzymanie stałych klientów w przedsiębiorstwie i pozyskanie nowych.

EN

The businesses reorganization process is not an easy task, since it forces people carrying look at the whole process from a different angle, from every possible point of view. This results in the constant search for new solutions that would achieve the desired result. The study attempted to reorganize the production process using data from the company, mapping the entire process taking into account the requirements of the customers. We analyzed the development of positions, additional employment for the needs of changes in the work on the production line. Changes are shown in the form of the proposed conditions in the mapping process. The reorganization of the production process will allow for the maintenance of regular customers in the enterprise and attracting new ones.

4

The Polish Hydrogeological Survey Database Integrator : a new GIS tool for the hydrogeological database management useful in mapping process

Cabalska J., Felter A., Hordejuk M., Mikołajczyk A.

Przegląd Geologiczny

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2005

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Vol. 53, nr 10/2

917-920

EN

The PSH Database Integrator as a sophisticated tool for groundwater management answers the needs of the Polish Hydrogeological Survey. At the first stage it is a functional integration system for all hydrogeological databases existing in the Polish Geological Institute such as those of Groundwater Monitoring, HYDRO Bank and Hydrogeological Map of Poland. The final stage of all kind of analysis involves cartographical representation of the results. Using the PSH Database Integrator in this process reduces labour input and time as well as helps keeping standards. Next stage in the system development will enable internet access for users.

5

Diagnosing corporate stability using grammatical evolution

Brabazon A., O'Neill M.

International Journal of Applied Mathematics and Computer Science

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2004

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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.