Feature Selection with Evolving, Fast and Slow Using Two Parallel Genetic Algorithms
This addresses feature selection challenges in machine learning for high-dimensional data, representing an incremental improvement through a hybrid method.
The paper tackled the problem of feature selection in high-dimensional data by proposing a new approach called Evolving Fast and Slow, which uses two parallel genetic algorithms with different mutation rates to simultaneously explore and exploit the search space, achieving very good results in accuracy and feature elimination.
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.