Membrane computing inspired genetic algorithm on multi-core processors

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Membrane computing is a branch of natural computing. Several studies have recently attempted to utilize the structure of membrane computing to improve intelligent algorithms. These studies have applied communication rules in membrane models to facilitate information exchange between membranes, thereby improving the performance of those algorithms. However, parallel membrane computing has not yet been considered. This study proposes a membrane computing-inspired genetic algorithm. Similar to previous studies, the algorithm also uses communication rules to facilitate information exchange. In this study, an appropriate membrane computing-inspired genetic algorithm is defined, in which each membrane can be executed over different cores in a parallel manner. The proposed algorithm can be executed over different cores and uses multi-core processing to implement parallel membrane computation. Simulation with a Colville minimization problem shows that the membrane computing inspired genetic algorithm has improved performance, with a mean error of the solution 61.9 times better than genetic algorithm.

Original languageEnglish
Pages (from-to)264-270
Number of pages7
JournalJournal of Computer Science
Volume9
Issue number2
DOIs
Publication statusPublished - 2013

Fingerprint

Genetic algorithms
Membranes
Communication
Processing

Keywords

  • Colville function
  • Genetic algorithms
  • Membrane computing
  • Multi-Core processing
  • Tissue P systems

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

Membrane computing inspired genetic algorithm on multi-core processors. / Maroosi, Ali; Muniyandi, Ravie Chandren.

In: Journal of Computer Science, Vol. 9, No. 2, 2013, p. 264-270.

Research output: Contribution to journalArticle

@article{f43149e43e8440f886a60933213fc08c,
title = "Membrane computing inspired genetic algorithm on multi-core processors",
abstract = "Membrane computing is a branch of natural computing. Several studies have recently attempted to utilize the structure of membrane computing to improve intelligent algorithms. These studies have applied communication rules in membrane models to facilitate information exchange between membranes, thereby improving the performance of those algorithms. However, parallel membrane computing has not yet been considered. This study proposes a membrane computing-inspired genetic algorithm. Similar to previous studies, the algorithm also uses communication rules to facilitate information exchange. In this study, an appropriate membrane computing-inspired genetic algorithm is defined, in which each membrane can be executed over different cores in a parallel manner. The proposed algorithm can be executed over different cores and uses multi-core processing to implement parallel membrane computation. Simulation with a Colville minimization problem shows that the membrane computing inspired genetic algorithm has improved performance, with a mean error of the solution 61.9 times better than genetic algorithm.",
keywords = "Colville function, Genetic algorithms, Membrane computing, Multi-Core processing, Tissue P systems",
author = "Ali Maroosi and Muniyandi, {Ravie Chandren}",
year = "2013",
doi = "10.3844/jcssp.2013.264.270",
language = "English",
volume = "9",
pages = "264--270",
journal = "Journal of Computer Science",
issn = "1549-3636",
publisher = "Science Publications",
number = "2",

}

TY - JOUR

T1 - Membrane computing inspired genetic algorithm on multi-core processors

AU - Maroosi, Ali

AU - Muniyandi, Ravie Chandren

PY - 2013

Y1 - 2013

N2 - Membrane computing is a branch of natural computing. Several studies have recently attempted to utilize the structure of membrane computing to improve intelligent algorithms. These studies have applied communication rules in membrane models to facilitate information exchange between membranes, thereby improving the performance of those algorithms. However, parallel membrane computing has not yet been considered. This study proposes a membrane computing-inspired genetic algorithm. Similar to previous studies, the algorithm also uses communication rules to facilitate information exchange. In this study, an appropriate membrane computing-inspired genetic algorithm is defined, in which each membrane can be executed over different cores in a parallel manner. The proposed algorithm can be executed over different cores and uses multi-core processing to implement parallel membrane computation. Simulation with a Colville minimization problem shows that the membrane computing inspired genetic algorithm has improved performance, with a mean error of the solution 61.9 times better than genetic algorithm.

AB - Membrane computing is a branch of natural computing. Several studies have recently attempted to utilize the structure of membrane computing to improve intelligent algorithms. These studies have applied communication rules in membrane models to facilitate information exchange between membranes, thereby improving the performance of those algorithms. However, parallel membrane computing has not yet been considered. This study proposes a membrane computing-inspired genetic algorithm. Similar to previous studies, the algorithm also uses communication rules to facilitate information exchange. In this study, an appropriate membrane computing-inspired genetic algorithm is defined, in which each membrane can be executed over different cores in a parallel manner. The proposed algorithm can be executed over different cores and uses multi-core processing to implement parallel membrane computation. Simulation with a Colville minimization problem shows that the membrane computing inspired genetic algorithm has improved performance, with a mean error of the solution 61.9 times better than genetic algorithm.

KW - Colville function

KW - Genetic algorithms

KW - Membrane computing

KW - Multi-Core processing

KW - Tissue P systems

UR - http://www.scopus.com/inward/record.url?scp=84880112961&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880112961&partnerID=8YFLogxK

U2 - 10.3844/jcssp.2013.264.270

DO - 10.3844/jcssp.2013.264.270

M3 - Article

AN - SCOPUS:84880112961

VL - 9

SP - 264

EP - 270

JO - Journal of Computer Science

JF - Journal of Computer Science

SN - 1549-3636

IS - 2

ER -