基于适应小波收缩的浮点数编码遗传算法

doi:10.11772/j.issn.1001-9081.2014.07.2071

计算机应用 ›› 2014, Vol. 34 ›› Issue (7): 2071-2073.DOI: 10.11772/j.issn.1001-9081.2014.07.2071

基于适应小波收缩的浮点数编码遗传算法

崔明义,邵超

河南财经政法大学计算机与信息工程学院,郑州 450002

收稿日期:2013-12-27 修回日期:2014-01-31 出版日期:2014-07-01 发布日期:2014-08-01
通讯作者: 崔明义
作者简介:崔明义(1958-),男,河南许昌人,教授,博士,主要研究方向:计算智能;邵超(1977-),男,河南三门峡人,副教授,博士,主要研究方向:人工智能。
基金资助:
国家自然科学基金资助项目;河南省科技攻关项目

Floating point representation genetic algorithm based on adaptive wavelet shrinkage

CUI Mingyi,SHAO Chao

School of Computer and Information Engineering, Henan University of Finance and Law, Zhengzhou Henan 450002, China

Received:2013-12-27 Revised:2014-01-31 Online:2014-07-01 Published:2014-08-01
Contact: CUI Mingyi
Supported by:
National Natural Science Foundation

摘要/Abstract

摘要：

通过独立同分布分析浮点数编码(FPR)噪声，用适应小波收缩的方法消除噪声对遗传算法性能的影响，在算法运行中用变异操作实现消噪。针对阈值变化对小波系数的影响，以单基因证明小波消噪变异的正确性；提出适应小波收缩构建软阈值函数，将函数运算植入算法的动态运行中；给出了具体的实现算法，用实例验证了算法的可行性。仿真实验表明，所提算法显著提高了收敛速度，收敛点与理论值相一致。

Abstract:

Noise of floating point representation was analyzed to be independent identically distributed. The noise was removed by adaptive wavelet shrinkage, to improve the performance of genetic algorithm. Denoising was implemented with mutation operation in algorithm running. Aiming at influence on wavelet coefficient with threshold variety, correctness of wavelet denoising mutation was proved with single gene. Soft threshold function was proposed with adaptive wavelet shrinkage, and the function was embedded into dynamic algorithm running. A concrete algorithm was given, and an example was used to verify the feasibility of the algorithm. The simulation experiment indicates the algorithm improves convergence rate, and the convergence point is the same as theoretical convergence point.

中图分类号:

崔明义邵超. 基于适应小波收缩的浮点数编码遗传算法[J]. 计算机应用, 2014, 34(7): 2071-2073.

CUI Mingyi SHAO Chao. Floating point representation genetic algorithm based on adaptive wavelet shrinkage[J]. Journal of Computer Applications, 2014, 34(7): 2071-2073.

参考文献

［1］ESHELMAN L, SCHAFFER J. Real-coded genetic algorithms and interval schemata［M］. San Francisco: Morgan Kaufmann Publishers, 1993:187-202.
［2］McCORMICK W T, SCHWEITZER P J, WHITE T W. Problem decomposition and data reorganization by a cluster technique［J］. Operations Research,1972, 20(5):993-1009.
［3］MICHALEWICZ Z. Genetic algorithm+data structure=evolution programs［M］. 3rd ed. Berlin: Springer-Verlag, 1996.
［4］WALTERS G A, SMITH D K. Evolutionary design algorithm for optimal layout of tree networks［J］. Engineering Optimization, 1995, 24(4):261-281.
［5］CUI M Y, SHANGGUAN Y L. Research on float-coded genetic algorithm based on wavelet denoising mutation［C］// Proceedings of the 3rd International Conference on Natural Computation. Piscataway: IEEE, 2007: 804-809.
［6］CUI M Y. An improved float-coded genetic algorithm based on wavelet denoising mutation［C］// Proceedings of the 7th World Congress on Intelligent Control and Automation. Piscataway: IEEE, 2008: 4018-4023.
［7］CUI M Y. FPRGA based on construction of multiwavelets in term of a novel transformation［C］// Proceedings of the 5th International Conference on Natural Computation. Piscataway: IEEE, 2009: 244-248.
［8］CUI M, SUN B. RWS-based floating point representation genetic algorithm［J］. Journal of Convergence Information Technology, 2012, 7(17):459-467.
［9］MARIANI V X, LUVIZOTTO G J, GUERRA F A. A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization［J］. Applied Mathematics and Computation, 2011, 217(15): 5822-5829.
［10］YOON Y, KIM Y-H, MORAGLIO S, et al. A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation［J］. Information Sciences, 2012, 183(1):48-65.
［11］CHANG D, ZHAO Y, ZHENG C, et al. A genetic clustering algorithm using a message-based similarity measure［J］. Expert Systems with Applications, 2012, 39(2): 2194-2202.
［12］TANG P-H, TSENG M-H. Adaptive directed mutation for real-coded genetic algorithms［J］. Applied Soft Computing, 2013,13(1):600-614.
［13］THAKUR M. A new genetic algorithm for global optimization of multimodal continuous functions［J］. Journal of Computational Science, 2013, 5(2):298-311.

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基于适应小波收缩的浮点数编码遗传算法

Floating point representation genetic algorithm based on adaptive wavelet shrinkage

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