  Variable selection problems generally present more than a single solution
and, sometimes, it is worth to find as many solutions as possible. The use of
Evolutionary Algorithms applied to this kind of problem proves to be one of the
best methods to find optimal solutions. Moreover, there are variants designed
to find all or almost all local optima, known as Niching Genetic Algorithms
(NGA). There are several different NGA methods developed in order to achieve
this task. The present work compares the behavior of eight different niching
techniques, applied to a climatic database of four weather stations distributed
in Tucuman, Argentina. The goal is to find different sets of input variables
that have been used as the input variable by the estimation method. Final
results were evaluated based on low estimation error and low dispersion error,
as well as a high number of different results and low computational time. A
second experiment was carried out to study the capability of the method to
identify critical variables. The best results were obtained with Deterministic
Crowding. In contrast, Steady State Worst Among Most Similar and Probabilistic
Crowding showed good results but longer processing times and less ability to
determine the critical factors.
