  Feature selection is one of the most challenging issues in machine learning,
especially while working with high dimensional data. In this paper, we address
the problem of feature selection and propose a new approach called Evolving
Fast and Slow. This new approach is based on using two parallel genetic
algorithms having high and low mutation rates, respectively. Evolving Fast and
Slow requires a new parallel architecture combining an automatic system that
evolves fast and an effortful system that evolves slow. With this architecture,
exploration and exploitation can be done simultaneously and in unison. Evolving
fast, with high mutation rate, can be useful to explore new unknown places in
the search space with long jumps; and Evolving Slow, with low mutation rate,
can be useful to exploit previously known places in the search space with short
movements. Our experiments show that Evolving Fast and Slow achieves very good
results in terms of both accuracy and feature elimination.
