The use of Artificial Immune Systems, computationally simulating some features of animal immune systems, can provide powerful tools for solving real optimization problems. In this project, we are developing variants of Clonal Selection Algorithm (CSA), focused on biological optimization problems; mixing different strategies inspired onby other meta-heuristics, mainly from Genetic Algorithm and Differential Evolution. To evaluate the proposal algorithm, we implemented benchmark functions, such as CEC 2005, and the results obtained so far show better performance when compared with the original CSA, as well as a very competitive performance when compared with other state-of-the-art evolutionary meta-heuristics. The full results will be submitted to Information Sciences jJournal soon.