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灾变机制下元胞遗传算法的选择压力研究 被引量:6

Selection pressure study of cellular genetic with disturbances
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摘要 对于遗传算法,全局探索和局部寻优之间的关系是算法好坏的核心问题,选择压力的变化直接影响着两者之间的平衡。研究了元胞遗传算法的选择压力,并在其灾变机制下进一步研究了其选择压力的变化规律,通过改变灾变规模和周期观察选择压的变化。灾变的发生使整个算法的选择压力降低,当灾变规模越大、周期越短,算法的选择压力也就越小。对于不同优化问题选择一个合适的选择压能使全局探索和局部寻优之间的平衡达到最佳化从而能够又快又精确地寻求到全局最优解。 With regard to Genetic Algorithm(GA),the exploration/exploitation trade-off is the key issue of the algorithm.The change of selection pressure affects their balance directly.This paper studies the selection pressure of cellular genetic algorithms and it also studies the selection pressure with disturbance,and observing the changes of selection pressure by changing the scale and cycle of disturbance.The disturbance weaken the selection pressure of the algorithm.When the disturbance has larger scale and shorter cycle,the algorithm has smaller selection pressure.Using an appropriate selection pressure enable the exploration/exploitation trade-off to achieve the best balance in order to find the global optimal solution quickly and accurately for different optimization problems.
出处 《计算机工程与应用》 CSCD 北大核心 2011年第27期32-35,97,共5页 Computer Engineering and Applications
基金 国家自然科学基金(No.60963002) 航空科学基金(No.2008ZD56003) 江西省教育厅科技研究项目(No.GJJ08209)~~
关键词 选择压力 灾变 元胞遗传算法 selection pressure disturbance cellular genetic algorithms
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参考文献14

  • 1Alba E, Dorronsoro B.Cellular genetic algorithms: Some theory: a selection pressure study on GAs[M].[S.l.]:Springer Science+Business Media LLC,2008:48-49.
  • 2Alba E, Luque G.Theoretical models of selection pressure for dEAs:Topology influence[C]//The 2005 IEEE Congress on Evolutionary Computation, 2005,1 : 214-221.
  • 3Simoncini D, Collard P,Verel S, et al.On the influence of selection operators on performances in cellular Genetic Algorithms[C]// Dans.Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, 2007 : 1-2.
  • 4Alba E, Troya J M.Cellular evolutionary algorithms: Evaluating the influence of ratio[C]//lnternational Conference on Parallel Problem Solving from Nature,Paris,France.Berlin:Springer,2000, 1917:29-38.
  • 5Giacobini M, Alba E, Tettamanzi A, et al.Modeling selection intensity for toroidal cellular evolutionary algorithms[C]//Genetic and Evolutionary Computation Conference,GECCO 2004.Berlin, Heidelberg: Springer, 2004,2723 : 3-11.
  • 6Giacobini M, Tettamanzi A, Tomassini M.Modelling selection intensity for linear cellular evolutionary algorithms[C]//LNCS 2936: Sixth International Conference, Evolution Artificielle, EA 2003, Marseille,France,October 2003:345-356.
  • 7Simoncini D,Verel S, Collard P,et al.Anisotropic selection in cellular genetic algorithms[C]//Genetic and Evolutionary Computation Conference,Washington,USA,Seatle,8-12 July 2006:559-566.
  • 8Kirley M.A cellular genetic algorithm with disturbance: Optimization using dynamic spatial interactions[J].Journal of Heuristics, 2002,8(3) :327-331.
  • 9Spiessens P, Manderick B,A massively parallel genetic algorithm: Implementation and first analysis[C]//1CGA, 1991 : 279-287,.
  • 10Jong K A D, Sarma J.On decentralizing selection algorithms[C]// the Sixth International Conference on Genetic Algorithms, 1995: 17-23.

二级参考文献27

  • 1龚涛,蔡自兴.自然计算的广义映射模型[J].计算机科学,2002,29(z1):27-29. 被引量:4
  • 2李茂军,罗安,童调生.人工免疫算法及其应用研究[J].控制理论与应用,2004,21(2):153-157. 被引量:43
  • 3CHIEN Steve,DECOSTE Dennis,DOYLE Richard,et al.Making an impact artificial intelligence at the jet propulsion laboratory [J].AI Magazine,1997,18(1):103-121.
  • 4MCCORMARK D M,Day R.How artificial intelligence impacts E&P productivity [J].World Oil,1993,214(10):6.
  • 5NG E Y K,FOK S C,PEH Y.C,et al.Computerized detection of breast cancer with artificial intelligence and thermograms [J].J of Medical Engineering and Technology,2002,26 (4):152-157.
  • 6BALLARD H D.Introduction to Natural Computation [M].Cambridge:The MIT Press,1997.
  • 7TRAUB J F,WERSCHULZ A G.Complexity and Information [M].Cambridge:Cambridge University Press,1998.
  • 8CAGAN Jonathan,GROSSMANN E Ignacio,HOOKER John.Conceptual framework for combining artificial intelligence and optimization in engineering design [J].Research in Engineering Design-Theory,Applications,and Concurrent Engineering,1997,9(1):20-34.
  • 9NOVAK E.Quantum complexity of integration [J].J Complexity,2001,17(1):2-16.
  • 10CHEUNG L C Y,HOLDEN T S I.Survey of artificial intelligence impacts on information systems engineering [J].Information and Software Technology,1991,33(7):499-508.

共引文献5

同被引文献47

  • 1朱朝艳,刘斌,李艺,张延年.离散变量桁架结构拓扑优化的杂交算法[J].东北大学学报(自然科学版),2004,25(8):800-803. 被引量:8
  • 2朱朝艳,刘斌,张延年,郭鹏飞.复合形遗传算法在离散变量桁架结构拓扑优化设计中的应用[J].四川大学学报(工程科学版),2004,36(5):6-10. 被引量:14
  • 3朱朝艳,刘斌,郭鹏飞,张延年.离散变量桁架结构拓扑优化的混合遗传算法[J].机械强度,2004,26(6):656-661. 被引量:8
  • 4杜海峰,公茂果,刘若辰,焦李成.自适应混沌克隆进化规划算法[J].中国科学(E辑),2005,35(8):817-829. 被引量:28
  • 5D Whitley. Cellular genetic algorithms [ C ]. Proc. of the Fifth In-ternational Conference on Genetic Algorithms (ICGA) , MorganKaufmann, 1993: 658 -659.
  • 6G Rudolph, J Sprave. A Cellular Genetic Algorithm with Self -Adjusting Acceptance Threshold [ C ]. Genetic Algorithms in Engi-neering Systems : Innovations and Applications on IEE, 1995 : 365-372.
  • 7G Folino, C Pizzuti, G Spezzano. A cellular genetic programmingapproach to classification[ C ]. Proc. of the Genetic and Evolution-ary Computation Conference ( GECCO - 99 ) , Morgan Kaufmann,1999: 1015 -1020.
  • 8W Kim, W Man, S Chi. Adding learning to cellular genetic algo-rithms for training recurrent neural networks [ J]. IEEE Transac-tions on Neural Networks, 1999,10(2) :239 -252.
  • 9D Bemabe, A Enrique. A Simple Cellular Genetic Algorithm forContinuous Optimization [ C ]. IEEE Congress on EvolutionaryComputation, 2006:2838 - 2844.
  • 10V Gordon, K Mathias, D Whitley. Cellular genetic algorithms asfunction optimizers : Locality effects [ C ]. In ACM Symposium onApplied Computing, 1994:237 - 241.

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