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CSA-DE/EDA: A Clonal Selection Algorithm Using Differential Evolution and Estimation of Distribution Algorithm Host Publication: The 9th International Conference on Brain Inspired Cognitive system Authors: Z. Li, Y. Xia and H. Sahli Publisher: Springer Publication Year: 2018 Number of Pages: 10 ISBN: 9783030005627
Abstract: 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.
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