  In this work we focus on the determination of the relative distributions of
young, intermediate-age and old populations of stars in galaxies. Starting from
a grid of theoretical population synthesis models we constructed a set of model
galaxies with a distribution of ages, metallicities and intrinsic reddening.
Using this set we have explored a new fitting method that presents several
advantages over conventional methods. We propose an optimization technique that
combines active learning with an instance-based machine learning algorithm.
Experimental results show that this method can estimate with high speed and
accuracy the physical parameters of the stellar populations.
