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A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework
Joint Authors
Elkhani, Naeimeh
Chandren Muniyandi, Ravie
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-19
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Elkhani, Naeimeh& Chandren Muniyandi, Ravie. 2017. A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework. Journal of Optimization،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1185883
Modern Language Association (MLA)
Elkhani, Naeimeh& Chandren Muniyandi, Ravie. A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework. Journal of Optimization No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1185883
American Medical Association (AMA)
Elkhani, Naeimeh& Chandren Muniyandi, Ravie. A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework. Journal of Optimization. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1185883
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1185883