CSA-DE/EDA: a Novel Bio-inspired Algorithm for Function Optimization and Segmentation of Brain MR Images

Abstract

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.