A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework. (19th April 2017)

• Record Type: Journal Article
• Authors: Elkhani, Naeimeh; Chandren Muniyandi, Ravie
• Other Names: Zhang Gexiang Academic Editor.
• Is Part Of: Journal of optimization. Volume 2017(2017)
• Publication Date: 2017-04-19
• Subjects: Mathematical optimization -- Periodicals; Structural optimization -- Periodicals; Mathematical optimization; Structural optimization; Periodicals; Electronic journals; 519.6
• DOI: 10.1155/2017/3259140
• Languages: English
• ISSNs: 2356-752X

Abstract

Abstract : Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism.