CSA-DE/EDA: a Novel Bio-inspired Algorithm for Function Optimization and Segmentation of Brain MR Images
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The clonal selection algorithm (CSA), which describes the basic features of an immune response to an antigenic stimulus, has drawn a lot of attention in the biologically inspired computing community, due to its highly adaptive and easy-to-implement nature. Despite many successful applications, CSA still suffers from limited ability to explore the solution space. In this paper, we incorporate the differential evolution (DE) algorithm and the estimation of distribution algorithm (EDA) into CSA, and thus propose a novel bio-inspired algorithm referred to as CSA-DE/EDA. In the proposed algorithm, the hypermutation and receptor editing processes are implemented based on DE and EDA, which provide improved local and global search ability, respectively. We have applied the proposed algorithm to five commonly used benchmark functions for optimization and brain magnetic resonance (MR) image segmentation. Our comparative experimental results show that the proposed CSA-DE/EDA algorithm outperforms several bio-inspired computing techniques. CSA-DE/EDA is a compelling bio-inspired algorithm for optimization tasks.
KeywordsBio-inspired computing Clonal selection algorithm (CSA) Differential evolution (DE) Estimation of distribution algorithm (EDA) Image segmentation
We appreciate the efforts devoted by the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) to collect and share the clinical MR brain data sets and their manual segmentations for comparing interactive and (semi)-automatic segmentation algorithms for MRI of major brain tissues.
This work was supported in part by the National Natural Science Foundation of China under Grant 61771397, in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grant JCYJ20180306171334997, in part by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University (NPU) under Grant ZZ2019029, in part by Synergy Innovation Foundation of the University and Enterprise for Graduate Students in NPU under Grant XQ201911, and in part by the the Project for Graduate Innovation team of NPU.
Compliance with Ethical Standards
Conflict of Interest
We would like to submit a manuscript entitled “CSA-DE/EDA: A Novel Bio-inspired Algorithm for Function Optimization and Segmentation of Brain MR Images” for possible publication in Cognitive Computation. There are no potential conflicts of interest to report.
This article does not contain any studies with human participants or animals performed by any of the authors.
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