| Resumen |
PhD thesis defended on 2nd December, 2013.
In general, the purpose of Global Optimization (GO) is to find the global optimum of an objective function defined inside a search space, and it has applications in many areas of science, engineering, economics, among other, where mathematical modeling is used. GO 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 often face great difficulties in solving GO problems. On the other hand, evolutionary methods are usually faster in locating a global optimum than deterministic ones, because they operate on a population of candidate solutions, so they have a bigger likelihood of finding the global optimum, and even they have a better adaptation to black box formulations or complicated function’ forms. |
