Earticle

초록

영어

Multiple sequence alignment (MSA) is an important tool in biological analysis. However, it is difficult to solve this class of problems, due to their exponential complexity. This paper presents an algorithm combining the genetic algorithm and a self-organizing neural network for solution to MSA. This approach demonstrates improved performance in long DNA and RNA data sets exhibiting small similarity.

목차

Abstract.
 1 Introduction
 2. GENETIC ALGORITHMS AND SELF-ORGANIZING NEURAL NETWORKS
 3 THE PROPOSED GA-SNN ALGORITHM
  3.1 Representation and Population Initialization
  3.2 Fitness Evaluation
  3.3. Selection and Terminations
  3.4. Operator Crossover and Mutation
  3.5 Self-organizing NN as a local search
 4. SIMULATION AND RESULTS
 5. CONCLUSION
 References

저자

• Shuting Wu [ The Research Center of Industrial Technology, School of Electronics & Information Engineering, ChonBuk National University, ]
• Malrey Lee [ The Research Center of Industrial Technology, School of Electronics & Information Engineering, ChonBuk National University, ]
• YongSeok Lee [ The Research Center of Industrial Technology, School of Electronics & Information Engineering, ChonBuk National University, ]
• Thomas M Gatton [ The School of Engineering and Technology, National University, ]

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