Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (7): 1972-1976.DOI: 10.11772/j.issn.1001-9081.2017.07.1972

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Encoding of genetic algorithm for a class of fitness functions

ZHU Chunmei1, MO Hongqiang2   

  1. 1. College of Mechanical and Electrical Engineering, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan Guangdong 528400, China;
    2. College of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510641, China
  • Received:2017-01-05 Revised:2017-03-04 Online:2017-07-10 Published:2017-07-18
  • Supported by:
    This work is partially supported by National Natural Science Foundation of China (61105062).


朱春媚1, 莫鸿强2   

  1. 1. 电子科技大学中山学院 机电工程学院, 广东 中山 528400;
    2. 华南理工大学 自动化科学与工程学院, 广州 510641
  • 通讯作者: 朱春媚
  • 作者简介:朱春媚(1981-),女,广东河源人,讲师,博士研究生,主要研究方向:智能控制、模式识别;莫鸿强(1976-),男,广东茂名人,教授,博士,主要研究方向:进化计算。
  • 基金资助:

Abstract: In the investigation of relationship between the periodicity of fitness function and encoding cardinality, the evaluation of encoding performance using the number of order-1 building blocks is not necessarily established. Focused on this issue, evaluating method of encoding performance of Genetic Algorithm (GA) using Accumulated Escape Probability (AEP) was proposed, and for a class of fitness functions linearly combined of sinusoidal functions whose frequencies are exponential to a positive integer m, research on encoding was carried out. Firstly, the general form of the fitness function was given, and the concept of base-m encoding was explained. Secondly, the definition of AEP was introduced, and a method was proposed to figure out AEPs. Then the AEPs of the above-mentioned fitness functions under encodings with different encoding bases were compared, and the results showed that, for a fitness function which was linearly combined of sinusoidal functions with frequencies exponential to a positive integer m, a base-m encoding could achieve higher AEP than encodings with bases other than m. The simulation results show that, the optimization performance and the rise time of the average fitness of the population under a base-m encoding are significantly better than those of the other encodings.

Key words: encoding, performance evaluation, Genetic Algorithm (GA), periodic fitness function, Accumulated Escape Probability (AEP)

摘要: 针对在探讨适应度函数的周期性特点与整数编码元数之间的关联特性时,一阶积木块数量对编码性能的评价不一定成立的问题,提出以累积逃脱概率(AEP)作为遗传算法(GA)编码性能的评价指标,对以频率为正整数m的整数次幂的正弦函数为基函数线性组合构成的适应度函数编码展开研究。首先给出了该类适应度函数的一般形式和m进制整数编码的含义;然后介绍了AEP的定义,并根据函数特点制定了AEP的计算方法;最后分析比较了该类适应度函数在不同整数编码下的AEP,指出其采用m元整数编码时更容易进化。仿真结果表明,该类适应度函数采用m元整数编码时,其最终优化结果和群体适应度均值的上升时间皆明显优于其他编码,反映了AEP能有效评价编码的性能,并再次验证了对于该类适应度函数m元整数编码优于非m元整数编码的结论。

关键词: 编码, 性能评价, 遗传算法, 周期性适应度函数, 累积逃脱概率

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