지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한기계학회 Journal of Mechanical Science and Technology Journal of Mechanical Science and Technology Vol.21 No.5
- 발행연도
- 2007.5
- 수록면
- 779 - 788 (10page)
이용수
초록· 키워드
오류제보하기
The immune system has pattern recognition capabilities based on reinforced learning, memory and affinity maturation interacting between antigens and antibodies. The paper deals with the adaptation of artificial immune system into genetic algorithm based design optimization. The present study utilizes the pattern recognition from the immune system and the evolution from genetic algorithm. The basic idea is derived from the fact that designs should be distinguished whether they are usable/feasible or infeasible and should be improved towards the optimal solution. For the expression of design solutions, binary coded strings are used to represent antigens and antibodies in artificial immune system and chromosomes in genetic algorithm. The paper discusses the procedure of constrained optimization that does not rely on any detailed mathematical formulation for constraint handling. A number of mathematical function minimization problems are examined for verification, and practical engineering optimization problems including inequality constraints are explored to support the proposed strategy.
목차
Abstract
1. Introduction
2. Immune system
3. GA based immune simulation
4. Proposed strategy
5. Illustrative examples
6. Engineering optimization
7. Closing remarks
Acknowledgments
References
참고문헌 (18)
참고문헌 신청
Coello Coello, C. A. and Cruz-Cortes, N., 2005, “Solving Multiobjective Optimization Problems Using an Artificial Immune System,” Genetic Programming and Evolvable Machines, 6: 163~190.
Coello Coello, C. A., Rivera, D. C., Cruz-Cortes, N., 2003, “Use of an Artificial Immune System for Job Shop Scheduling,” Lecture Notes in Computer Science. 203, Springer, Berlin/Heidelberg.
de Castro, L. N. and Timmis, J., 2002a, “Artificial Immune System: A New Computational Intelligence Approach,” Springer, Great Britain.
de Castro, L. N. and Timmis, J., 2002b, “Artificial Immune Systems: A Novel Paradigm for Pattern Recognition,” Alonso, L., Corchado, J., Fyfe, C., (Eds.), Artificial Neural Networks in Pattern Recognition, University of Paisley.
de Castro, L. N. and Von Zuben, F. J., 2002c, “Learning and Optimization Using the Clonal Selection Principle,” IEEE Transactions on Evolutionary Computation, 6 (3): 239~251.
Coello Coello, C. A., Rivera, D. C., Cruz-Cortes, N., 2003, “Use of an Artificial Immune System for Job Shop Scheduling,” Lecture Notes in Computer Science. 203, Springer, Berlin/Heidelberg.
de Castro, L. N. and Timmis, J., 2002a, “Artificial Immune System: A New Computational Intelligence Approach,” Springer, Great Britain.
de Castro, L. N. and Timmis, J., 2002b, “Artificial Immune Systems: A Novel Paradigm for Pattern Recognition,” Alonso, L., Corchado, J., Fyfe, C., (Eds.), Artificial Neural Networks in Pattern Recognition, University of Paisley.
de Castro, L. N. and Von Zuben, F. J., 2002c, “Learning and Optimization Using the Clonal Selection Principle,” IEEE Transactions on Evolutionary Computation, 6 (3): 239~251.
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