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基于子树交换交叉的遗传编程通用模式理论:第二部分。

General schema theory for genetic programming with subtree-swapping crossover: Part II.

作者信息

Poli Riccardo, McPhee Nicholas Freitag

机构信息

Department Computer Science, University of Essex, Colchester, CO4 3SQ, UK.

出版信息

Evol Comput. 2003 Summer;11(2):169-206. doi: 10.1162/106365603766646825.

DOI:10.1162/106365603766646825
PMID:12875668
Abstract

This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Cartesian node reference system, introduced in Part I, and on the notion of a variable-arity hyperschema, introduced here, which generalises previous definitions of a schema. The theory includes two main theorems describing the propagation of GP schemata: a microscopic and a macroscopic schema theorem. The microscopic version is applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. Therefore, this theorem is applicable to Koza's GP crossover with and without uniform selection of the crossover points, as well as one-point crossover, size-fair crossover, strongly-typed GP crossover, context-preserving crossover and many others. The macroscopic version is applicable to crossover operators in which the probability of selecting any two crossover points in the parents depends only on the parents' size and shape. In the paper we provide examples, we show how the theory can be specialised to specific crossover operators and we illustrate how it can be used to derive other general results. These include an exact definition of effective fitness and a size-evolution equation for GP with subtree-swapping crossover.

摘要

本文是一篇分为两部分的论文的第二部分,第一部分(Poli和McPhee,2003年)介绍了一种用于遗传编程(GP)的带有子树交换交叉算子的通用模式理论。与最近其他的GP模式理论结果一样,该理论给出了下一代中模式实例期望数量的精确公式(而非下限)。该理论基于第一部分引入的笛卡尔节点参考系统,以及此处引入的可变元超模式概念,后者对先前的模式定义进行了推广。该理论包含两个描述GP模式传播的主要定理:微观模式定理和宏观模式定理。微观版本适用于用一个父代的子树替换另一个父代的子树以产生子代的交叉算子。因此,该定理适用于带有和不带有交叉点均匀选择的Koza的GP交叉算子,以及单点交叉、尺寸公平交叉、强类型GP交叉、上下文保留交叉等许多其他交叉算子。宏观版本适用于这样的交叉算子,即选择父代中任意两个交叉点的概率仅取决于父代的大小和形状。在本文中,我们给出了示例,展示了该理论如何专门应用于特定的交叉算子,并说明了如何用它来推导其他一般性结果。这些结果包括有效适应度的精确定义以及带有子树交换交叉算子的GP的大小演化方程。

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