National Journal of System and Information Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Multilayer Architecture of Parallel-Genetic-Fuzzy System:A Case of Effective Transportation for Co-Operatives in India


Affiliations
1 Post Graduate Department of Computer Science, Sardar Patel University, India
     

   Subscribe/Renew Journal


The paper discusses broad architecture of parallel execution of genetic-fuzzy system by identifying limitations of the single minded traditional genetic algorithms. For additional advantages to manage uncertainty as well as other advantages related with fuzzy logic, fuzzification is also incorporated in the approach. The propose architecture of the hybrid genetic-fuzzy systems is experimented in the domain of dairy co-operatives and sample encoding, genetic operations, fitness functions and fuzzification are discussed for the case. An interface screen is also presented to demonstrate working of the prototype system. At end, advantages and future scope of the proposed work is presented.
Subscription Login to verify subscription
User
Notifications
Font Size


  • A.S. Wu, H. Yu, S. Jin, K.-C. Lin, G. Schiavone. (2004). An incremental genetic algorithm approach to multiprocessor scheduling. IEEE Trans. Parallel Distrib. Syst. 15 , 824-834.

  • Akerkar R A and Sajja Priti Srinivas. (2009). Knowledge Based Systems. MA, USA: Jones & Bartlett Publishers.

  • Bethke, A. (1976). Comparison of genetic algorithms and gradient-based optimizers on parallel processors:efficiency of use of processing capacity. Tech. Rep. No. 197, University of Michigan, Logic of Computers Group, Ann Arbor, MI.

  • Cantu-Paz, E. (1997). Designing efficient master–slave parallel genetic algorithms. Technical L Report No. 97004, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL.

  • E. Alba, F. Luna, A.J. Nebro, J.M. Troya. ((2004) ). Parallel heterogeneous genetic algorithms for continuous optimization . Parallel Computing 30 , 699-719.

  • Fatma A. Omaraa, Mona M. Arafa. (2010). Genetic algorithms for task scheduling problem . Journal of Parallel Distrib. Comput. 70 , 13-22.

  • Grefenstette, J. (1981). Parallel adaptive algorithms for function optimization. Tech. Rep. No. CS-81-19, Vanderbilt University, Computer Science Department, Nashville, TN.

  • José A. Moral-Muñoz, Manuel J. Cobo, Francisco Chiclana, Andrew Collop, and Enrique Herrera-Viedma. (2015). Analyzing Highly Cited Papers in Intelligent. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 1-9.

  • Matthew Barth, Guoyuan Wu, and Kanok Boriboonsomsin . (March 2016). Intelligent Transportation Systems Show Promise in. National Centre for sustainable Transportation, Center for Environmental Research and Technology (CE-CERT) University of California, Riverside,.

  • Mohammadreza Ghatreh Samani, Seyyed-Mahdi Hosseini-Motlagh. (vol 5, isssue 1, 2017 ). A Hybrid Algorithm for a Two-Echelon Location-Routing Problem with Simultaneous Pickup and Delivery under Fuzzy Demand. International Journal of Transportation Engineering.

  • Tanese, R. (1989). Distributed genetic algorithm, . Proceedings of Third International Conference on Genetic algorithms , pp. 434-439.

  • Tzung-Pei Hong, Yeong-Chyi Lee, Min-Thai Wu. (2014). An effective parallel approach for genetic-fuzzy data mining. Expert Systems with Applications, Volume 41, Issue 2 , 655-662.

  • Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 338-358.


Abstract Views: 254

PDF Views: 6




  • Multilayer Architecture of Parallel-Genetic-Fuzzy System:A Case of Effective Transportation for Co-Operatives in India

Abstract Views: 254  |  PDF Views: 6

Authors

Priti S. Sajja
Post Graduate Department of Computer Science, Sardar Patel University, India

Abstract


The paper discusses broad architecture of parallel execution of genetic-fuzzy system by identifying limitations of the single minded traditional genetic algorithms. For additional advantages to manage uncertainty as well as other advantages related with fuzzy logic, fuzzification is also incorporated in the approach. The propose architecture of the hybrid genetic-fuzzy systems is experimented in the domain of dairy co-operatives and sample encoding, genetic operations, fitness functions and fuzzification are discussed for the case. An interface screen is also presented to demonstrate working of the prototype system. At end, advantages and future scope of the proposed work is presented.

References