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Variable Structure Representation Based Genetic Programming and Its Application in Self-Adaptive Modeling

机译:基于变结构表示的遗传规划及其在自适应建模中的应用

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In the application of genetic programming, the tree-like individual often adopts 'S' expressions in the Lisp language to be presented, but this kind of coding method is inconvenient for genetic operations. This thesis proposed an effective representation based on the structure data and symbol objects of Matlab, using structures and symbol objects to express the tree-like individual expressions. Each individual structure has four fields: left, right, operator and value, the first two stands for the left and right branches of the individual tree, the field of "operator" is a symbol vector including the operators or functions which connect the elements in left and right branches, the last field of "value" is used to store the fitness value of each individual. The whole population can be expressed by a structure array whose number of elements equals to the population size. The size of individual structure may be changed during genetic operations. To keep the diversity of the population, a re-initialization process is combined with the evolution. At the end of this thesis, we established a model to analyze the relation between the total values of imports & exports of China from 1985-1997 and the corresponding number of containers used, then compared the results with 'S' expression based GP and the normal methods in data modeling including linear regression and two-order polynomial regression. Experimental results indicate that variable structure based GP can express the individual solution more directly and effectively than those mentioned.
机译:在遗传编程的应用中,树状个体经常采用Lisp语言中的'S'表示来表达,但是这种编码方法对遗传操作不方便。本文提出了一种基于Matlab的结构数据和符号对象的有效表示方法,利用结构和符号对象来表达树状单个表达式。每个单独的结构都有四个字段:左,右,运算符和值,前两个代表单个树的左右分支,“运算符”字段是一个符号向量,其中包括连接运算符或元素的函数。在左右分支中,“值”的最后一个字段用于存储每个人的适应度值。整个种群可以由一个结构数组表示,其元素数量等于种群大小。个体结构的大小可能会在遗传操作中发生变化。为了保持种群的多样性,将重新初始化过程与进化过程结合在一起。本文的最后,我们建立了一个模型,分析了1985-1997年中国进出口总值与相应使用的集装箱数量之间的关系,然后将结果与基于S表达式的GP和数据建模中的常规方法,包括线性回归和二阶多项式回归。实验结果表明,基于可变结构的GP可以比上述方法更直接,更有效地表达单个解决方案。

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