  This paper presents a procedure to add broader diversity at the beginning of
the evolutionary process. It consists of creating two initial populations with
different parameter settings, evolving them for a small number of generations,
selecting the best individuals from each population in the same proportion and
combining them to constitute a new initial population. At this point the main
loop of an evolutionary algorithm is applied to the new population. The results
show that our proposal considerably improves both the efficiency of previous
methodologies and also, significantly, their efficacy in most of the data sets.
We have carried out our experimentation on twelve data sets from the UCI
repository and two complex real-world problems which differ in their number of
instances, features and classes.
