[1]周树德,孙增圻.遗传算法中的联结关系[J].智能系统学报,2009,4(6):483-489.[doi:10.3969/j.issn.1673-4785.2009.06.003]
ZHOU Shu-de,SUN Zeng-qi.Linkage in genetic algorithms[J].CAAI Transactions on Intelligent Systems,2009,4(6):483-489.[doi:10.3969/j.issn.1673-4785.2009.06.003]
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ZHOU Shu-de,SUN Zeng-qi.Linkage in genetic algorithms[J].CAAI Transactions on Intelligent Systems,2009,4(6):483-489.[doi:10.3969/j.issn.1673-4785.2009.06.003]
遗传算法中的联结关系
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
4
期数:
2009年第6期
页码:
483-489
栏目:
学术论文—人工智能基础
出版日期:
2009-12-25
- Title:
- Linkage in genetic algorithms
- 文章编号:
- 1673-4785(2009)06-0483-07
- Keywords:
- genetic algorithm; linkage; fitness function; Fourier analysis
- 分类号:
- TP18
- 文献标志码:
- A
- 摘要:
- 进化计算领域的一个根本问题是哪些问题适合遗传算法求解，为此需要研究问题的结构对算法性能的影响.变量之间的联结关系是问题的本质属性，决定了遗传算法求解问题的难度.如果某个变量对函数值的影响非线性依赖于其他变量，则认为这些变量之间存在联结关系不，对遗传算法的联结关系这一理论问题进行了深入研究，给出了分析一般离散问题联结结构的理论基础，通过分析傅里叶系数与函数子空间的关系，提出了检测黑箱问题联结结构的确定性和随机性算法，通过试验分析说明了算法的正确性和有效性.
- Abstract:
- One of the most challenging and fundamental problems in the field of evolutionary computation is identification of the classes of problems for which genetic algorithms are especially well (or ill) suited. This is closely related to the question of how the structure of the fitness landscape affects the performance of genetic algorithms. The linkage is referred to as a nonlinear interaction between variables. This is the intrinsic characteristic of the optimization problem, determining the degree of difficulty in solving it. The authors focused on the linkage problem with genetic algorithms and were able to establish a theoretical foundation for the analysis of linkage structures. Based on Fourier analysis of problem structure, it was proven that mask strings with nonzero Fourier coefficients accurately reflect linkage structure. A deterministic and stochastic algorithm for identifying the linkage structure of black-box problems was discussed and experimental results verified its correctness and efficiency.
- 参考文献/References:
-
［1］THIERENS D. Scalability problems of simple genetic algorithms［J］.Evolutionary Computation, 1999, 7(4):331-352.
［2］GOLDBERG D E. The design of innovation: lessons from and for competent genetic algorithms［M］. Boston: Kluwer Academic Publishers, 2002:88-95.
［3］MENON A. Frontiers of evolutionary computation［M］. London: Kluwer Publisher， 2004:23-27.
［4］周树德.遗传算法中联结关系检测的理论和方法研究［D］.北京：清华大学，2007.
????????? ZHOU Shude. Research on theory and methods for linkage detection in genetic algorithms［D］. Beijing：Tsinghua University,2007.
［5］DAVIDOR Y. Epistasis variance: a viewpoint on GA-hardnes［C］//Proceedings of the 6th International Conference on Genetic Algorithms. San Mateo:Morgan Kaufmann, 1995:133-138.
［6］CHEN Y P. Extending the scalability of linkage learning genetic algorithms: theory and practice［D］. Champaign: UIUC, 2004.
［7］HECKENDORN R B. Embedded landscapes［J］. Evolutionary Computation, 2002, 10(4):345-369.
［8］HECKENDORN R B, WRIGHT A. Efficient linkage discovery by limited probing［J］. Evolutionary Computation， 2004, 12(4)：517-545.
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备注/Memo
收稿日期：2009-04-15.
基金项目：国家自然科学基金资助项目（60736023，60674053，90716021）.
作者简介：
周树德，男，1980年生，博士，主要研究方向为智能优化理论和算法、复杂系统分析与设计.发表学术论文10余篇.
孙增圻，男，1943年生，教授，博士生导师，主要研究方向为智能控制、机器人、模糊系统和神经网络、计算机控制理论及应用等.发表学术论文300余篇.
更新日期/Last Update:
2010-02-17