Feature Selection with Evolving, Fast and Slow Using Two Parallel Genetic Algorithms

05/11/2020
by   Uzay Cetin, et al.
0

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.

READ FULL TEXT
research
03/17/2023

SFE: A Simple, Fast and Efficient Feature Selection Algorithm for High-Dimensional Data

In this paper, a new feature selection algorithm, called SFE (Simple, Fa...
research
09/20/2016

GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms

Boosted cascade of simple features, by Viola and Jones, is one of the mo...
research
08/06/2014

New crossover operators for multiple subset selection tasks

We have introduced two crossover operators, MMX-BLXexploit and MMX-BLXex...
research
03/09/2017

Fast Genetic Algorithms

For genetic algorithms using a bit-string representation of length n, th...
research
12/07/2021

GraphPAS: Parallel Architecture Search for Graph Neural Networks

Graph neural architecture search has received a lot of attention as Grap...
research
07/24/2023

Semi-Lagrangian Scheme with Arakawa Splitting for Gyro-kinetic Equations

The gyro-kinetic model is an approximation of the Vlasov-Maxwell system ...
research
11/28/2010

DXNN Platform: The Shedding of Biological Inefficiencies

This paper introduces a novel type of memetic algorithm based Topology a...

Please sign up or login with your details

Forgot password? Click here to reset