Earticle

초록

영어

Aiming at the traditional GEP algorithm adopted fixed rate of mutation and crossover rate in the process of evolution, and ignored the dynamic change of individual fitness, which leaded to the presence of premature convergence and local optimization problem. By using the cloud adaptive strategy and cloud cross strategy of cloud model, a genetic algorithm based on cloud model (Cloud Model Gene Expression Programming, CMGEP) was proposed. The algorithm adjusted the mutation rate and crossover rate in evolution through the cloud adaptation strategy according to the change of dynamic, and timely calculated population similarity to achieve cloud cross to increase the diversity of population and jump out of the premature convergence. It was applied to the field of railway engineering and its results were compared with those obtained by traditional GEP Algorithm and CMGEP Algorithm. Experiments show that the algorithm can improve the adaptability and the prediction accuracy, it has better convergence.

목차

Abstract
 1. Introduction
 2. Basic Concept
 3. GEP Algorithm Based on Cloud Model (CM-GEP)
  3.1. Cloud Adaptive Strategies
  3.2. Cloud Crossover Strategy
 4. Prediction Model of CMGEP Algorithm
  4.1. Prediction Model
  4.2. Comparative Analysis of Prediction Results
 5. Conclusion
 References

키워드

저자

• Zhang Rui [ School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang, China ]
• Hou Shasha [ School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang, China ]
• Gao Hui [ Chengdu Donglu Traffic Science and Technology Co., Ltd. Chengdu, Sichuan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605