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常微分方程组并行演化建模的实验研究
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Supported by the National Natural Science Foundation of China under Grant Nos.70071042, 60073043, 60133010 (国家自然科学基金); the Assisting Project of Ministry of Education of China for Backbone Teachers of University and College (国家教育部高等学校骨干教师资助计划); the Natural Science Foundation of Hubei Province of China under Grant No.2001ABB062 (湖北省自然科学基金); the Youth Chenguang Project of Science and Technology of Wuhan City of China under Grant No.20015005037 (武汉市青年科技晨光计划); the Program of the State Key Labortory of Parallel and Distrbuted Processing of China (并行与分布处理国家重点实验室资助项目)


Experimental Study on the Parallel Evolutionary Modeling of System of Ordinary Differential Equations
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    摘要:

    以常微分方程组的演化建模问题为主要研究对象,设计了分布式异步并行演化算法,并以128台PIII 500微机通过10Mbps的以太网互联而成的机群系统作为模拟实验环境进行了大规模的并行实验,系统地测试了算法中的一些重要的并行控制参数,包括处理机间的连通度、个体的迁移率和迁移代频等对算法性能的影响,得到了一些崭新的实验结果,给出了一些结果分析,特别是对串行算法和并行算法的最好建模结果进行了比较.

    Abstract:

    A distributed asynchronous parallel evolutionary modeling algorithm for solving the evolutionary modeling problem of system of ordinary differential equations is proposed in this paper. Performed on a simulated parallel environment consisting of 128 Pentium III 500 PCs connected by a 10Mbps Ethernet, large-scale experiments have been done to systematically test the effect of some important parallel control parameters, which include the degree of connectivity between processors, the migration rate and the migration interval of individuals, on the performance of the algorithm. Many new experimental results are achieved as well as some analysis and explanations are given. Especially the best modeling results for the sequential algorithm and the parallel one are compared at the end.

    参考文献
    1]Koza JR. Genetic Programming: on the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press, 1992. [2]Koza JR. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA: MIT Press, 1994. [3]Holland JH. Adaptation in Natural and Artificial System. Ann Arbor, MI: University of Michigan Press, 1975. [4]Kang LS, Cao HQ, Chen YP. The evolutionary modeling algorithm for system of ordinary differential equations. Chinese Journal of Computers, 1999,22(8):871～876 (in Chinese with English Abstract). [5]Cao HQ, Kang LS, Chen YP. A hybrid evolutionary modeling algorithm for dynamic systems. Journal of Computer Research and Development, 1999,36(8):923～931 (in Chinese with English Abstract). [6]Cao HQ, Kang LS, Chen YP. The modeling of system of ordinary differential equations for dynamic systems: the experimental study based on different search techniques. Journal of Computer Research and Development, 2001,38(6):746～753 (in Chinese with English Abstract). [7]Cao HQ, Kang LS, Chen YP, Yu JX. Evolutionary modeling of system of ordinary differential equations with genetic programming. Genetic Programming and Evolvable Machines, 2000,1(4):309～337. [8]Bethke AD. Comparison of genetic algorithms and gradient-based optimizers on parallel processors: efficiency of use of processing capacity. Technical Report, No.197, Ann Arbor, MI: University of Michigan, 1976. [9]Cantú-Paz E. A survey of parallel genetic algorithms. IlliGAL Technical Report, No.97003, Urbana, IL: Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 1997. [10]Koza JR, Andre D. Parallel genetic programming on a network of transputers. Technical Report, No.CS-TR-95-1542, Department of Computer Science, Stanford University, 1995. [11]Nordin P, Hoffmann F, Francone FD, Brameier M, Banzhaf W. AIM-GP and parallelism. In: Angeline PJ, Michalewicz Z, Schoenauer M, Yao X, Zalzala A, eds. Proceedings of the Congress on Evolutionary Computation, Vol 2. Piscataway, NJ: IEEE Press, 1999. 1059～1066. [12]Punch WF, Zongker D, Goodman ED. The royal tree problem: a benchmark for single and multiple population genetic programming. In: Angeline PJ, JrKinnear KE, eds. Advances in Genetic Programming 2. Cambridge, MA: MIT Press, 1996. 299～316. [13]Cantú-Paz E. Topologies, migration rates, and multi-population parallel genetic algorithms. IlliGAL Technical Report, No.99007. Urbana, IL: Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 1999. [14]康立山,曹宏庆,陈毓屏.常微分方程组的演化建模.计算机学报,1999,22(8):871～876. [15]曹宏庆,康立山,陈毓屏.动态系统的演化建模.计算机研究与发展,1999,36(8):923～931. [16]曹宏庆,康立山,陈毓屏.动态系统的常微分方程组建模基于不同搜索技术的实验研究.计算机研究与发展,2001,38(6): 746～753.
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曹宏庆,康立山,陈毓屏,胡庆丰.常微分方程组并行演化建模的实验研究.软件学报,2003,14(3):443-450

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  • 收稿日期:2002-02-05
  • 最后修改日期:2002-02-05
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