ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory
This paper presents a variation in the algorithm EMODS (Evolutionary Metaheuristic of Deterministic Swapping), at the level of its mutation stage in order to train algorithms for each problem. It should be noted that the EMODS metaheuristic is a novel framework that allows multi-objective optimizati...
- Autores:
-
Ruiz-Rangel, Jonathan
Ardila Hernandez, Carlos Julio
Maradei Gonzalez, Luis
Jabba Molinares, Daladier
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/1863
- Acceso en línea:
- http://hdl.handle.net/20.500.12442/1863
- Palabra clave:
- Finite Deterministic Automaton
Artificial Neural Networks
Genetic Algorithm
EMODS
Backpropagation Algorithm
Conjugate Gradient Algorithm
Levenberg-Marquardt Algorithm
- Rights
- License
- Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.eng.fl_str_mv |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory |
| title |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory |
| spellingShingle |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory Finite Deterministic Automaton Artificial Neural Networks Genetic Algorithm EMODS Backpropagation Algorithm Conjugate Gradient Algorithm Levenberg-Marquardt Algorithm |
| title_short |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory |
| title_full |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory |
| title_fullStr |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory |
| title_full_unstemmed |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory |
| title_sort |
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory |
| dc.creator.fl_str_mv |
Ruiz-Rangel, Jonathan Ardila Hernandez, Carlos Julio Maradei Gonzalez, Luis Jabba Molinares, Daladier |
| dc.contributor.author.none.fl_str_mv |
Ruiz-Rangel, Jonathan Ardila Hernandez, Carlos Julio Maradei Gonzalez, Luis Jabba Molinares, Daladier |
| dc.subject.eng.fl_str_mv |
Finite Deterministic Automaton Artificial Neural Networks Genetic Algorithm EMODS Backpropagation Algorithm Conjugate Gradient Algorithm Levenberg-Marquardt Algorithm |
| topic |
Finite Deterministic Automaton Artificial Neural Networks Genetic Algorithm EMODS Backpropagation Algorithm Conjugate Gradient Algorithm Levenberg-Marquardt Algorithm |
| description |
This paper presents a variation in the algorithm EMODS (Evolutionary Metaheuristic of Deterministic Swapping), at the level of its mutation stage in order to train algorithms for each problem. It should be noted that the EMODS metaheuristic is a novel framework that allows multi-objective optimization of combinatorial problems. The proposal for the training of neural networks will be named ERNEAD (training of Evolutionary Neural Networks through Evolutionary Strategies and Finite Automata). The selection process consists of five phases: the initial population generation phase, the forward feeding phase of the network, the EMODS search phase, the crossing and evaluation phase, and finally the verification phase. The application of the process in the neural networks will generate sets of networks with optimal weights for a particular problem. ERNEAD algorithm was applied to two typical problems: breast cancer and flower classification, the solution of the problems were compared with solutions obtained by applying the classical Backpropagation, Conjugate Gradient and Levenberg-Marquardt algorithms. The analysis of the results indicated that ERNEAD produced more precise solutions than the ones thrown by the classic algorithms. |
| publishDate |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2018-03-13T16:08:10Z |
| dc.date.available.none.fl_str_mv |
2018-03-13T16:08:10Z |
| dc.date.issued.none.fl_str_mv |
2018 |
| dc.type.spa.fl_str_mv |
article |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.identifier.issn.none.fl_str_mv |
09740635 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/20.500.12442/1863 |
| identifier_str_mv |
09740635 |
| url |
http://hdl.handle.net/20.500.12442/1863 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
| dc.rights.license.spa.fl_str_mv |
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional |
| rights_invalid_str_mv |
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
| dc.publisher.spa.fl_str_mv |
Editorial Board |
| dc.source.eng.fl_str_mv |
International Journal of Artificial Intelligence Vol. 16, No.1 (2018) |
| institution |
Universidad Simón Bolívar |
| dc.source.uri.spa.fl_str_mv |
http://www.ceser.in/ceserp/index.php/ijai/article/view/5456 |
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Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_16ecRuiz-Rangel, Jonathan6bcede93-7c56-43e9-af54-4ace50e87aad-1Ardila Hernandez, Carlos Julio6376bedb-3192-4777-9e34-c269ee81d567-1Maradei Gonzalez, Luis0d1df5a0-18d3-42e2-a1f0-5e92ec42b5b4-1Jabba Molinares, Daladiera6449168-983a-4292-aef6-a59465add02a-12018-03-13T16:08:10Z2018-03-13T16:08:10Z201809740635http://hdl.handle.net/20.500.12442/1863This paper presents a variation in the algorithm EMODS (Evolutionary Metaheuristic of Deterministic Swapping), at the level of its mutation stage in order to train algorithms for each problem. It should be noted that the EMODS metaheuristic is a novel framework that allows multi-objective optimization of combinatorial problems. The proposal for the training of neural networks will be named ERNEAD (training of Evolutionary Neural Networks through Evolutionary Strategies and Finite Automata). The selection process consists of five phases: the initial population generation phase, the forward feeding phase of the network, the EMODS search phase, the crossing and evaluation phase, and finally the verification phase. The application of the process in the neural networks will generate sets of networks with optimal weights for a particular problem. ERNEAD algorithm was applied to two typical problems: breast cancer and flower classification, the solution of the problems were compared with solutions obtained by applying the classical Backpropagation, Conjugate Gradient and Levenberg-Marquardt algorithms. The analysis of the results indicated that ERNEAD produced more precise solutions than the ones thrown by the classic algorithms.engEditorial BoardInternational Journal of Artificial IntelligenceVol. 16, No.1 (2018)http://www.ceser.in/ceserp/index.php/ijai/article/view/5456Finite Deterministic AutomatonArtificial Neural NetworksGenetic AlgorithmEMODSBackpropagation AlgorithmConjugate Gradient AlgorithmLevenberg-Marquardt AlgorithmERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theoryarticlehttp://purl.org/coar/resource_type/c_6501Brownlee, J. 2011. Clever Algorithms. Nature Inspired Programming Recipes, Vol. 5 of Machine Learning, Prentice Hall, Swinburne University, Melbourne, Australia.Cardie, C. 1993. 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Hibridizacion de lógica difusa y algoritmos genéticos en la predicción de registros de velocidad de ondas. campo guafita.Nieto Parra, H. 2011. Diseño e implementacióon de una metaheurística hibrida basada en recocido simulado, algoritmos geneticos y teoría de autómatas para la optimización bi- objetivo de problemas combinatorios.Ruiz-Rangel, J. R. 2011. Entrenamiento de redes neuronales artificiales basado en algoritmo evolutivo y teoría de autómatas finitos. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. 1986. 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