Using multi-objective evolutionary algorithms to optimize paths on a data network applying fuzzy operators for crossover and mutation functions
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Autónoma del Caribe
2007
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| _version_ | 1876443866334232576 |
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| author | Jesús Rafael Cohen Jlménez |
| author_facet | Jesús Rafael Cohen Jlménez |
| contents | Using multi-objective evolutionary algorithms to optimize paths on a data network applying fuzzy operators for crossover and mutation functions Jesús Rafael Cohen Jlménez Ingeniería fuzzy sets Fuzzy logic MOEA (Multi GA (Genetics algorithms) Objective Evolutionary Algorithms) Two ingredients of soft computing, evolutionary computing and fuzzy logic can be combined in a way that makes them benefit from one another. An evolutionary algorithm can evolve fuzzy systems, while fuzzy logic can be used to control evolution to speed up convergence to a global optimum and escape from local optimum. Be- sides, concepts that are inherent in the workings of evolutionary algorithms, Iike fitness, can be fuzzified, thus taking advantage of a tolerance for imprecision in order to save computational resources. 2007 artículo científico 1692-8261 https://www.redalyc.org/articulo.oa?id=496251109002 en http://www.redalyc.org/revista.oa?id=4962 PROSPECTIVA application/pdf Universidad Autónoma del Caribe PROSPECTIVA (Colombia) Num.1 Vol.5 |
| format | Artículo científico |
| id | redalyc_496251109002 |
| institution | Redalyc |
| language | en |
| publishDate | 2007 |
| publisher | Universidad Autónoma del Caribe |
| spellingShingle | Using multi-objective evolutionary algorithms to optimize paths on a data network applying fuzzy operators for crossover and mutation functions Jesús Rafael Cohen Jlménez Ingeniería fuzzy sets Fuzzy logic MOEA (Multi GA (Genetics algorithms) Objective Evolutionary Algorithms) Using multi-objective evolutionary algorithms to optimize paths on a data network applying fuzzy operators for crossover and mutation functions Jesús Rafael Cohen Jlménez Ingeniería fuzzy sets Fuzzy logic MOEA (Multi GA (Genetics algorithms) Objective Evolutionary Algorithms) Two ingredients of soft computing, evolutionary computing and fuzzy logic can be combined in a way that makes them benefit from one another. An evolutionary algorithm can evolve fuzzy systems, while fuzzy logic can be used to control evolution to speed up convergence to a global optimum and escape from local optimum. Be- sides, concepts that are inherent in the workings of evolutionary algorithms, Iike fitness, can be fuzzified, thus taking advantage of a tolerance for imprecision in order to save computational resources. 2007 artículo científico 1692-8261 https://www.redalyc.org/articulo.oa?id=496251109002 en http://www.redalyc.org/revista.oa?id=4962 PROSPECTIVA application/pdf Universidad Autónoma del Caribe PROSPECTIVA (Colombia) Num.1 Vol.5 |
| title | Using multi-objective evolutionary algorithms to optimize paths on a data network applying fuzzy operators for crossover and mutation functions |
| topic | Ingeniería fuzzy sets Fuzzy logic MOEA (Multi GA (Genetics algorithms) Objective Evolutionary Algorithms) |
| url | https://www.redalyc.org/articulo.oa?id=496251109002 |