FPGA on FPGA: Implementation of Fine-grained Parallel Genetic Algorithm on Field Programmable Gate Array

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. KeywordsGenetic algorithm, FPGA, TSP, Parallel Processing.

[1]  Zdenek Konfrst,et al.  Parallel Genetic Algorithms: Advances, Computing Trends, Applications and Perspectives , 2004, IPDPS.

[2]  Xiaodong Li,et al.  The effects of varying population density in a fine-grained parallel genetic algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Yoonho Seo,et al.  Discrete Optimization An efficient genetic algorithm for the traveling salesman problem with precedence constraints , 2002 .

[4]  Jonatan Gómez,et al.  Maintaining genetic diversity in fine-grained parallel genetic algorithms by combining cellular automata, Cambrian explosions and massive extinctions , 2010, IEEE Congress on Evolutionary Computation.

[5]  A. M. Wahdan,et al.  Introducing an FPGA based genetic algorithms in the applications of blind signals separation , 2003, The 3rd IEEE International Workshop on System-on-Chip for Real-Time Applications, 2003. Proceedings..

[6]  Xue Shengjun,et al.  The Analysis and Research of Parallel Genetic Algorithm , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[7]  Zhong-Xian Chi,et al.  An Efficient Fine-grained Parallel Genetic Algorithm Based on GPU-Accelerated , 2007, 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007).

[8]  A. Bargiela,et al.  Fine-grained parallel genetic algorithm: a sto­chastic optimisation method , 1997 .

[9]  Asim Munawar,et al.  Optimization of parallel Genetic Algorithms for nVidia GPUs , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[10]  Stephen Marshall,et al.  FPGA realisation of the genetic algorithm for the design of grey-scale soft morphological filters , 2003 .

[11]  Silvio E. Barbin,et al.  Real-time performance considerations of an FPGA-embedded genetic algorithm for self-recovery of an antenna array , 2010, 2010 Conference Proceedings ICECom, 20th International Conference on Applied Electromagnetics and Communications.

[12]  Ying Huang,et al.  A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy , 2012, 2012 8th International Conference on Natural Computation.