TY - JFULL AU - Pei-Chann Chang and Wei-Hsiu Huang and Ching-Jung Ting and Hwei-Wen Luo and Yu-Peng Yu PY - 2010/6/ TI - Self-evolving Artificial Immune System via Developing T and B Cell for Permutation Flow-shop Scheduling Problems T2 - International Journal of Mathematical and Computational Sciences SP - 571 EP - 577 VL - 4 SN - 1307-6892 UR - https://publications.waset.org/pdf/13384 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 41, 2010 N2 - Artificial Immune System is applied as a Heuristic Algorithm for decades. Nevertheless, many of these applications took advantage of the benefit of this algorithm but seldom proposed approaches for enhancing the efficiency. In this paper, a Self-evolving Artificial Immune System is proposed via developing the T and B cell in Immune System and built a self-evolving mechanism for the complexities of different problems. In this research, it focuses on enhancing the efficiency of Clonal selection which is responsible for producing Affinities to resist the invading of Antigens. T and B cell are the main mechanisms for Clonal Selection to produce different combinations of Antibodies. Therefore, the development of T and B cell will influence the efficiency of Clonal Selection for searching better solution. Furthermore, for better cooperation of the two cells, a co-evolutional strategy is applied to coordinate for more effective productions of Antibodies. This work finally adopts Flow-shop scheduling instances in OR-library to validate the proposed algorithm. ER -