A particular steady-state new strategy of evolution is studied in this paper. After comparison with GA and ES, we specially focus our attention on the choice of genetic operators, the way to apply them and finally how each generation is built from the previous one. Knowing that it is not possible to reach a universal heuristic able to choose the genetic operators and to manage them, we present a method where the genetic operators themselves are evaluated according to their performance. Thanks to this method, the improvement observed in order to optimize classical functions and the no relevant trials for adaptive rates, brings us to combine it with a very simple steady state algorithm with a small sized population, and we conclude by a recommendation about parameters like population size, updating and clearing rates.