A new decoding method of the grammatical evolution

Zhengheng Yan, Pei He, Wei-Zhong Huang, Su Liu
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Abstract

GE (grammatical Evolution) is an evolutionary algorithm that evolves programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules from the grammar definition of the Backs-Naur form(BNF) are used for the program in the genotype-phenotype mapping process. However, the matter relating to the completeness of individual phenotype throughout population formation isn't well self-addressed, so limiting both the convergence speed and the accuracy of the evaluations to some extent. In this paper, we propose an improved GE algorithm that aims to guarantee the completeness of individual phenotypes during initialization as well as subsequent evolution. A comparison of the improved algorithm (IGE) with the classical GE algorithm (CGE) and NGE(integer-coded grammatical evolution) conducted on the symbolic regression problems shows that the improved algorithm (IGE) not only reduces the search space and improves the accuracy of the algorithm, but also speeds up the convergence of the algorithm in constructing both. Definition 1. “Incomplete”: An individual is called an incomplete individual if its corresponding sentential form contains a non-terminal symbol, and its corresponding mapping process is called incomplete mapping. Definition 2. "Recursive" production rule: Refers to the BNF in which the same non-terminal symbol appears on the left and right sides of the production rule. Definition 3. Combined production rule: For a production rule with a non-terminal symbol in the right part, if it can be followed by only one type of production rule, it is said that the production rule and its subsequent followable production rule are combined production rules. Definition 4. Non-combined production rule: Production rule except for combined production rule
一种新的语法演变解码方法
GE(语法进化)是一种进化算法,它使用可变长度的二进制字符串在任意语言中进化程序。二进制基因组决定了在基因型-表型定位过程中,哪些来自Backs-Naur形式(BNF)语法定义的产生规则被用于程序。然而,在整个种群形成过程中,个体表型的完整性问题并没有得到很好的自我解决,从而在一定程度上限制了收敛速度和评价的准确性。在本文中,我们提出了一种改进的GE算法,旨在保证初始化过程中个体表型的完整性以及随后的进化。将改进算法(IGE)与经典的GE算法(CGE)和整数编码语法进化算法(NGE)在符号回归问题上的比较表明,改进算法(IGE)不仅缩小了搜索空间,提高了算法的精度,而且在构建两者时加快了算法的收敛速度。定义1。“不完全”:如果一个个体对应的句子形式包含一个非终结符号,则称为不完全个体,其对应的映射过程称为不完全映射。定义2。“递归”产生规则:指在产生规则的左右两侧出现相同的非终结符号的BNF。定义3。组合产生式规则:对于右侧有非终结符号的产生式规则,如果该产生式规则后面只能有一种产生式规则,则称该产生式规则及其后续的产生式规则为组合产生式规则。定义4。非组合产生规则:除组合产生规则外的其他产生规则
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