一种基于FPGA的自适应遗传算法
An FPGA Based Adaptive Genetic Algorithm
-
摘要: 采用了一种适合硬件实现的自适应遗传算法，利用种群的最大适应度fmax﹑最小适应度fmin和适应度平均值fave这3个变量来自适应地控制整个种群的交叉概率pc 和变异概率pm 。选用了适合硬件实现的选择﹑交叉﹑变异算子，并将它们设计成流水线结构, 同时，将选择算子与适应度计算并行化，大大提高了算法的运行效率。整个设计采用了XILINX公司的XC2V1000型号的FPGA芯片。算法利用VHDL语言来描述。实现后的测试表明，这种自适应遗传算法明显改善了算法的搜索性能和全局收敛性，同时利用硬件实现有效减少了运行时间，使其在一些实时性要求较高的场合得到应用成为可能。
-
关键词:
- 自适应遗传算法;并行; FPGA
Abstract: A hardware implement Adaptive Genetic Algorithm (AGA) is proposed in this paper. The adaptive algorithm uses three parameters, i. e. fmax , fmin and fave to determine the fc and fm of the whole generation adaptively. The selection , crossover and mutation operators which are suitable for hardware implement are selected and they are designed in a pipelining architecture . The parallelism of the selection operator and the computation of the fitness of the individual enhance the efficiency of the algorithm greatly. The hardware GA processor has been implemented in XILINX FPGA(Field Programmable Gate Arrays) XC2V1000. The VHDL language is used to describe the whole algorithm. Experimental results indicate that the adaptive genetic algorithm improves the global convergence and search performance of the algorithm greatly. The hardware implementation of the algorithm reduces the running time efficiently and makes it possible to apply in time-critical systems. -
潘正君，康立山，陈毓屏. 演化计算[M]. 北京：清华大学出版社，1998: 3-4.[2]Petridis V, Kazarlis S, Bakirtzis A. Varying fitness function in genetic algorithm constrained optimization: The cutting stock and unit commitment problems[J]. IEEE Trans. on SMC Part B: Cybernetics , 1998, 28 (5): 629-639.[3]Shaunna M, Tom L, Abdulla H. A gene- tic algorithm environme- mnt for star pattern recognition[J]. Journal of Intelligent and Fuzzy Systems, 1998, 6(1): 3-16.[4]Bhandarkar S M , Zhang H.Image seg- ment using evolutionary computation[J]. IEEE Trans. on Evolutionary Computation,1999, 3 (1): 1-21.[5]唐加福，汪定伟，高振，等. 面向非线性规划问题的混合式遗传算法[J]. 自动化学报，2000，26(3): 401-404.[6]骆晨钟，邵惠鹤. 采用混沌变异的进化算法[J]. 控制与决策，2000，15(5): 557-560.[7]Scrinvas M, Patnaik L M. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms . IEEE Trans. on SMC, 1994, 24(4): 656-667.[8]王蕾，沈庭芝，招扬. 一种改进的自适应遗传算法[J]. 系统工程与电子技术, 2002, 24(5): 75-78. -
计量
- 文章访问数: 2052
- HTML全文浏览量: 50
- PDF下载量: 783
- 被引次数: 0
下载:
