The week's pick
Reseach Article
FPGA on FPGA: Implementation of Fine-grained Parallel Genetic Algorithm on Field Programmable Gate Array
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 80 - Number 6 |
| Year of Publication: 2013 |
| Authors: A. Al-marakeby |
10.5120/13867-1725
|
A. Al-marakeby . FPGA on FPGA: Implementation of Fine-grained Parallel Genetic Algorithm on Field Programmable Gate Array. International Journal of Computer Applications. 80, 6 ( October 2013), 29-32. DOI=10.5120/13867-1725
Abstract
Many optimization problems have complex search space, which either increase the solving problem time or finish searching without obtaining the best solution. Genetic Algorithm (GA) is an optimization technique used in solving many practical problems in science, engineering, and business domains. Parallel Genetic Algorithm (PGA) has been widely used to increase speed of GA, especially after the spread of parallel platforms such as GPUs, FPGA, and Multi-Core Processors. In this paper, we introduce a type of PGA called Fine-grained Parallel Genetic Algorithm, which has the advantages of maintaining better population diversity, and inhibiting premature. Fine-grained PGA is implemented on Field Programmable Gate Array, and the system is used to solve the classical TSP problem. The results show the advantages of the Fine-grained PGA over sequential GA, and the advantages of Field Programmable Gate Array as a parallel platform.
References
- A. Muhammad, A. Bargiela, G. King, Fine-Grained Parallel Genetic Algorithm: A Stochastic Optimisation Method, Proc. of 1st World Congress on Systems Simulation, p. 199-203, Singapore, September 1997
- Chiung Moon , Jongsoo Kim , Gyunghyun Choi , Yoonho Seo , An efficient genetic algorithm for the traveling salesman problem with precedence constraints, European Journal of Operational Research -140 (2002).
- Giovanni Cantor , Jonatan Gómez, Maintaining Genetic Diversity in Fine-grained Parallel Genetic Algorithms by Combining Cellular Automata, Cambrian Explosions and Massive Extinctions, IEEE Congress on Evolutionary Computation , 2010
- H. Emam , M. A. Ashour , H. Fekry , A. M. Wahdan Introducing an FPGA based - genetic algorithms in the applications of blind signals separation, Proceedings of The 3rd IEEE International Workshop on System-on-Chip for Real-Time Applications 2003
- Jian-Ming Li, Xiao-Jing Wang, Rong-Sheng He, Zhong-Xian Chi , An Efficient Fine-grained Parallel Genetic Algorithm Based on GPU-Accelerated, International Conference on Network and Parallel Computing - 2007
- Miroslav Joler, Damir Malnar, and Silvio E. Barbin, Real-Time Performance Considerations of an FPGA-Embedded Genetic Algorithm for Self-Recovery of an Antenna Array, ICECom, Conference Proceedings, 2010
- Mohamed Wahib ,Asim Munawar, Masaharu Munetomo , Kiyoshi Akama, Optimization of Parallel Genetic Algorithms for nVidia GPUs, IEEE Congress on Evolutionary Computation (CEC), 2011
- M S Hamid and S Marshall, FPGA Realisation Of The Genetic Algorithm For The Design Of Grey-Scale Soft Morphological Filters, International Conference on Visual Information Engineering, 2003.
- Wei Li , Ying Huang ,A Distributed Parallel Genetic Algorithm Oriented Adaptive Migration Strategy, 8th International Conference on Natural Computation, 2012
- Xiaodong Li, Michael Kirley, The Effects of Varying Population Density in a Fine-,grained Parallel Genetic Algorithm, Proceedings of the Congress on Evolutionary Computation, 2002.
- XUE Shengjun , GUO Shaoyong , BAI Dongling, The Analysis and Research of Parallel Genetic Algorithm. Wireless Communications, Networking and Mobile Computing, 2008 .
- Zden?ek Konfr?st ,Parallel Genetic Algorithms: Advances, Computing Trends, Applications and Perspectives , Proceedings of the 18th International Parallel and Distributed Processing Symposium 2004.
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