论文标题
使用两种平行遗传算法的发展，快速和慢的特征选择
Feature Selection with Evolving, Fast and Slow Using Two Parallel Genetic Algorithms
论文作者
论文摘要
特征选择是机器学习中最具挑战性的问题之一，尤其是在使用高维数据时。在本文中，我们解决了特征选择的问题，并提出了一种称为快速发展的新方法。这种新方法基于使用两种具有高突变率和低突变率的平行遗传算法。快速发展和缓慢的发展需要一个新的并行体系结构，结合了一个自动系统，该系统可以快速发展，并且努力发展的系统会发展缓慢。借助此架构，可以同时且一致进行探索和剥削。快速发展，具有高突变率，对于探索跳远的搜索空间中的新不知名的地方可能很有用。并且以低突变率的速度发展缓慢，可用于利用以前已知的搜索空间中以短运动的方式来利用以前已知的地方。我们的实验表明，在准确性和功能消除方面，快速发展的快速和缓慢的发展取得了非常好的效果。
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.
