Earticle

초록

영어

The appropriate choice of crossover and mutation rates is critical to the success of genetic algorithms. Earlier researches focused on finding optimal non-dynamic crossover or mutation rates, which vary for different problems, and different stages of the genetic process in a problem. This paper proposed an ameliorate adaptive genetic algorithm where the mutation and crossover rates are adapted dynamically based on the evaluation results of the respective offspring in the next generation. Simultaneously, we combined this new algorithm with PD controllers to improve the stability of control systems. The experimental results are compared with other researchers’ approaches in this field. The PD-GA method can also significantly accelerate the system response and induce lower overshoot as well.

목차

Abstract
 1. Introduction
 2. Adaptive Genetic Algorithm
  2.1. Mutation and Crossover
  2.2. Algorithm Scheme
 3. Adaptive GA Method
 4. Experimental Results
 5. Conclusions
 Acknowledgement
 References

키워드

저자

• Xing shuli [ Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, Chon Buk National University, 664-14, 1Ga, Deokjin-Dong,Jeonju, Chon Buk, Korea ]
• HyangRan Lee [ Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, Chon Buk National University, 664-14, 1Ga, Deokjin-Dong,Jeonju, Chon Buk, Korea ]
• Malrey Lee [ Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, Chon Buk National University, 664-14, 1Ga, Deokjin-Dong,Jeonju, Chon Buk, Korea ] corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605