TY - GEN
T1 - CSA-DE/EDA
T2 - 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018
AU - Li, Zhe
AU - Xia, Yong
AU - Sahli, Hichem
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - The clonal selection algorithm (CSA), which describes the basic features of an immune response to an antigenic stimulus, has drawn a lot of research attention in the bio-inspired computing community, due to its highly-adaptive and easy-to-implement nature. However, despite many successful applications, this optimization technique still suffers from limited ability to explore the solution space. In this paper, we incorporate the differential evolution (DE) and estimation of distribution algorithm (EDA) into CSA, and thus propose a novel bio-inspired computing algorithm called CSA-DE/EDA. In this algorithm, the hypermutaion and receptor editing processes are implemented based on DE and EDA, which provide improved local and global search ability, respectively. We have applied this algorithm to brain image segmentation. Our comparative experimental results suggest that the proposed CSA-DE/EDA algorithm outperforms several bio-inspired computing techniques on the segmentation problem.
AB - The clonal selection algorithm (CSA), which describes the basic features of an immune response to an antigenic stimulus, has drawn a lot of research attention in the bio-inspired computing community, due to its highly-adaptive and easy-to-implement nature. However, despite many successful applications, this optimization technique still suffers from limited ability to explore the solution space. In this paper, we incorporate the differential evolution (DE) and estimation of distribution algorithm (EDA) into CSA, and thus propose a novel bio-inspired computing algorithm called CSA-DE/EDA. In this algorithm, the hypermutaion and receptor editing processes are implemented based on DE and EDA, which provide improved local and global search ability, respectively. We have applied this algorithm to brain image segmentation. Our comparative experimental results suggest that the proposed CSA-DE/EDA algorithm outperforms several bio-inspired computing techniques on the segmentation problem.
KW - Bio-inspired computing
KW - Clonal selection algorithm (CSA)
KW - Differential evolution (DE)
KW - Estimation of distribution algorithm (EDA)
KW - Image segmentation
UR - http://www.scopus.com/inward/record.url?scp=85055083692&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00563-4_28
DO - 10.1007/978-3-030-00563-4_28
M3 - 会议稿件
AN - SCOPUS:85055083692
SN - 9783030005627
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 293
EP - 302
BT - Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings
A2 - Hussain, Amir
A2 - Luo, Bin
A2 - Zheng, Jiangbin
A2 - Zhao, Xinbo
A2 - Liu, Cheng-Lin
A2 - Ren, Jinchang
A2 - Zhao, Huimin
PB - Springer Verlag
Y2 - 7 July 2018 through 8 July 2018
ER -