원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
vol.1 no.1
2008.12
pp.21-30
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보