Earticle

초록

영어

Intelligent test sheet generation is a multi-objective constrained optimization problem. Genetic algorithm based on groups search strategy can provide a better solution for multi-objective optimization. Traditional genetic algorithm in test sheet generation process has many drawbacks, such as poor convergence, low fitness and high exposure times. To solve these problems, this paper proposes an adaptive cellular genetic algorithm based on selection strategy. Selection strategy can adaptively determine candidate test items set and the conceptual granularities according to the desired concept scope. Then, a new cellular population is formed by candidate test items. After evolution by the rule, genetic algorithms are executed. The experimental results show that the proposed algorithm gets rid of tests that do not meet the requirements which can reduce knowledge related errors, lower the exposure of tests, and increase the possibility of escape from local optima. In general, the algorithm proposed in this paper effectively improves the convergence speed as well as generates test papers more in line with people's demands.

목차

Abstract
 1. Introduction
 2. Selection Strategy Determination of Concept Correlation Degree
 3. Cellular Automata and Mathematical Model of Test Sheet Generation Problem
  3.1. Principle of Cellular Automata
  3.2. Mathematical Model of Test Sheet Generation Problem
 4. Author Name(S) And Affiliation(S)
 4. Adaptive Cellular Genetic Algorithm Based on Selection Strategy
  4.1 Determination of Cellular Space and Coding Scheme
  4.2. Parameter Settings
  4.3. Determination of the Fitness Function
  4.4.Steps of Adaptive Cellular Genetic Test Sheet Generation Algorithm Based on Selection Strategy
 5. Second and Following Pages Experiment and Analysis
  5.1. Experimental Methods
  5.2. Analysis of Experimental Results
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Ankun Huang [ School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China ]
• Dongmei Li [ School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China ]
• Jiajia Hou [ School of Information, Renmin University of China, Beijing 100872, China ]
• Tao Bi [ School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605