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== Abstract == | == Abstract == | ||
− | In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and | + | In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application. |
Document type: Part of book or chapter of book | Document type: Part of book or chapter of book | ||
== Full document == | == Full document == | ||
− | <pdf>Media: | + | <pdf>Media:Lopez-Garcia_et_al_2016b-beopen1241-4086-document.pdf</pdf> |
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The different versions of the original document can be found in: | The different versions of the original document can be found in: | ||
− | * [https://zenodo.org/record/164849 https://zenodo.org/record/164849] under the license | + | * [https://zenodo.org/record/164849 https://zenodo.org/record/164849] under the license https://creativecommons.org/licenses/by-nc-nd |
− | * [https://zenodo.org/record/164849/files/MAEB.pdf https://zenodo.org/record/164849/files/MAEB.pdf] under the license | + | * [https://zenodo.org/record/164849/files/MAEB.pdf https://zenodo.org/record/164849/files/MAEB.pdf] under the license http://creativecommons.org/licenses/by-nc-nd/4.0/legalcode |
− | * [http://link.springer.com/content/pdf/10.1007/978-3-319-44636-3_27 http://link.springer.com/content/pdf/10.1007/978-3-319-44636-3_27],[http://dx.doi.org/10.1007/978-3-319-44636-3_27 http://dx.doi.org/10.1007/978-3-319-44636-3_27] under the license | + | * [http://link.springer.com/content/pdf/10.1007/978-3-319-44636-3_27 http://link.springer.com/content/pdf/10.1007/978-3-319-44636-3_27], |
+ | : [http://dx.doi.org/10.1007/978-3-319-44636-3_27 http://dx.doi.org/10.1007/978-3-319-44636-3_27] under the license cc-by-nc-nd | ||
− | * [https://link.springer.com/chapter/10.1007/978-3-319-44636-3_27 https://link.springer.com/chapter/10.1007/978-3-319-44636-3_27],[https://rd.springer.com/chapter/10.1007/978-3-319-44636-3_27 https://rd.springer.com/chapter/10.1007/978-3-319-44636-3_27],[https://core.ac.uk/display/144755094 https://core.ac.uk/display/144755094],[https://academic.microsoft.com/#/detail/2514081068 https://academic.microsoft.com/#/detail/2514081068] | + | * [https://link.springer.com/chapter/10.1007/978-3-319-44636-3_27 https://link.springer.com/chapter/10.1007/978-3-319-44636-3_27], |
+ | : [https://www.scipedia.com/public/Lopez-Garcia_et_al_2016b https://www.scipedia.com/public/Lopez-Garcia_et_al_2016b], | ||
+ | : [https://dx.doi.org/10.1007/978-3-319-44636-3_27 https://dx.doi.org/10.1007/978-3-319-44636-3_27], | ||
+ | : [http://dx.doi.org/10.1007/978-3-319-44636-3_27 http://dx.doi.org/10.1007/978-3-319-44636-3_27], | ||
+ | : [https://dblp.uni-trier.de/db/conf/caepia/caepia2016.html#Lopez-GarciaOOM16 https://dblp.uni-trier.de/db/conf/caepia/caepia2016.html#Lopez-GarciaOOM16], | ||
+ | : [https://rd.springer.com/chapter/10.1007/978-3-319-44636-3_27 https://rd.springer.com/chapter/10.1007/978-3-319-44636-3_27], | ||
+ | : [https://core.ac.uk/display/144755094 https://core.ac.uk/display/144755094], | ||
+ | : [https://academic.microsoft.com/#/detail/2514081068 https://academic.microsoft.com/#/detail/2514081068] under the license http://www.springer.com/tdm |
In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application.
Document type: Part of book or chapter of book
The different versions of the original document can be found in:
Published on 01/01/2016
Volume 2016, 2016
DOI: 10.1007/978-3-319-44636-3_27
Licence: CC BY-NC-SA license
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