| Data: |
2014 |
| Resum: |
In general, the purpose of Global Optimization (GO) is finding the global optimum of an objective function defined inside a search space. The GO has applications in many areas of science, engineering, economics, among other, where mathematical models are utilized. Those algorithms are divided into two groups: deterministic, and evolutionary. Since deterministic methods only provide a theoretical guarantee of locating local minimums of the objective function, they face great difficulties in solving GO problems. On the other hand, evolutionary methods are faster in locating a global optimum than deterministic ones, because they operate over a population of candidate solutions, therefore they have a bigger likelihood of finding the global optimum, and a better adaptation to black box formulations or complicated function forms. |
| Nota: |
Advisors: Erik Cuevas, Humberto Sossa. Date and location of the PhD thesis defense: 2nd December 2013, Centro de Investigación en Computación - Instituto Politécnico Nacional. |
| Drets: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Llengua: |
Anglès |
| Document: |
Altres ; recerca ; Versió publicada |
| Publicat a: |
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 1-3, ISSN 1577-5097 |
visitant ::
identificació
