Abstract
Genetic algorithms (GA) have been shown to be effective in the optimization of many large-scale real-world problems in a reasonable amount of time. Parallel GAs not only reduce the overall GA execution time, but also bring higher quality solutions due to parallel search in multiple parts of the solution space. This paper proposes a parallel GA system on hardware such as Field-Programmable-Gate-Arrays (FPGAs). Our approach targets multiple FPGAs by exploring different search areas of the same solution space with different behaviours. Each FPGA contains an optimised customisable GA which can be configured using run-time parameters, removing the need for expensive recompilation. This paper also explores adjustment of the migration gap, providing empirical guidance on good settings to users. Experiments on three problems show the high performance of our system, with a 30 times speedup achieved compared to a multi-core CPU-based implementation.
- M. Mitchell, "An Introduction to Genetic Algorithms", Bradford Book, 1998. Google ScholarDigital Library
- G. Luque, and E.Alba, "Parallel Genetic Algorithms: Theory and Real World Applications", Springer, Vol. 37, 2011. Google ScholarDigital Library
- S. Scott, S. Ashok, and S. Shared, "HGA: A hardware-based genetic algorithm", ACM International Symposium on Field-Programmable Gate Arrays, pp. 53--59, 1995. Google ScholarDigital Library
- M. Vavouras, K. Papadimitriou, I. Papaefstathiou, "High-speed FPGA-based implementations of a Genetic Algorithm", International Symposium on Systems, Architectures, Modeling, and Simulation (SAMOS), pp. 9--16, 2009. Google ScholarDigital Library
- J. Pimery, and K. Pinit, "Development of a flexible hardware core for genetic algorithm", Intelligent Computing and Intelligent Systems, Vol. 1, pp. 867--870, 2009.Google Scholar
- C. Effraimidis, K. Papadimitriou, A. Dollas, and I. Papaefstathiou, "A self-reconfiguring architecture supporting multiple objective functions in genetic algorithms", International Conference on Field Programmable Logic and Applications (FPL), pp. 453--456, 2009.Google ScholarCross Ref
- P.R. Fernando, R. Zebulum, and A. Stoica, "Customisable FPGA IP core implementation of a general-purpose genetic algorithm engine", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 1, pp. 133--149, 2010. Google ScholarDigital Library
- L. Guo, D. B. Thomas, and W. Luk, "Customisable Architectures for the Set Covering Problem", HEART, pp. 69--74, 2013.Google Scholar
- M.A. Vega-Rodriguez, R. Gutierrez-Gil, J.M. Avila-Roman, J.M. Sanchez-Perez, and J.A. Gomez-Pulido, "Genetic algorithms using parallelism and FPGAs: the TSP as case study", International Conference Workshops on Parallel Processing, pp. 573--759, 2005 Google ScholarDigital Library
- T. Tachibana et. al., "General architecture for hardware implementation of genetic algorithm", IEEE Symposium on Field-Programmable Custom Computing Machines, pp. 291--292, 2006. Google ScholarDigital Library
- N. Yoshida, and T. Yasuoka, "Multi-gap: parallel and distributed genetic algorithms in VLSI", Systems, Man and Cybernetics, Vol. 5, pp. 571--576, 1999.Google Scholar
- Y. Choi, and D. J. Cheung, "VLSI processor of parallel genetic algorithm", IEEE Asia Pacific Conference on ASIC, pp. 143--1462, 2000.Google Scholar
- Y. Jewajinda, and P. Chongstitvatana, "FPGA implementation of a celullar compact genetic algorithm", NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp. 385--390, 2008. Google ScholarDigital Library
- T. Kamimura, and A. Kanasugi, "A parallel processor for distributed genetic algorithm with redundant binary number", 6th International Conference on New Trends in Information Science and Service Science and Data Mining (ISSDM), pp. 125--128, 2012.Google Scholar
- M. S. Jelodar et.al., "SOPC-based parallel genetic algorithm", IEEE Congress on Evolutionary Computation, pp. 2800--2806, 2006.Google Scholar
- L. Guo, D. B. Thomas, C. Guo, and W. Luk,, "Automated framework for FPGA-based parallel genetic algorithms", 24th International Conference on Field Programmable Logic and Applications (FPL), pp. 1--7, 2014.Google ScholarCross Ref
- Dos Santos, P.V., Alves, J.C., Ferreira, J.C., "A scalable array for Cellular Genetic Algorithms: TSP as case study", International Conference on Reconfigurable Computing and FPGAs (ReConFig), pp. 1--6, 2012.Google ScholarCross Ref
- D. A. Coley, "An Introduction to Genetic Algorithms for Scientists and Engineers", World Scientific, 1999. Google ScholarDigital Library
- R. L. Haupt, and S. E. Haupt, "Practical Genetic Algorithms", John Wiley and Sons, 2004. Google ScholarDigital Library
- J. Newborough, and S. Stepney, "A generic framework for population-based algorithms, implemented on multiple FPGAs", Artificial Immune Systems, Springer Berlin Heidelberg, pp. 43--55, 2005. Google ScholarDigital Library
- OpenSPL Consortium, The OpenSPL Standard, v1.0, http://www.openspl.org/Google Scholar
Index Terms
- Parallel Genetic Algorithms on Multiple FPGAs
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