论文标题
语法引导进化计算中的初始化和语法设计
Initialisation and Grammar Design in Grammar-Guided Evolutionary Computation
论文作者
论文摘要
语法提供了一种方便而有力的机制，可以定义各种问题的可能解决方案的空间。但是，当用于语法进化（GE）时，必须在语法设计时要格外小心，以确保基因型与表型映射的多态性性质不会阻碍搜索。此外，最近的工作强调了初始化方法对GE性能的重要性。尽管最近的工作阐明了初始化和语法设计有关GE的问题，但它们对其他方法的影响，例如随机搜索和无上下文的语法遗传编程（CFG-GP），在很大程度上是未知的。本文使用几种不同的初始化例程和语法设计研究了在一系列基准问题下进行GE，随机搜索和CFG-GP。结果表明，与GE和随机搜索相比，CFG-GP对初始化和语法设计不太敏感：我们还证明，通过简单调整调谐参数来管理观察到的CFG-GP性能差的病例。我们得出的结论是，CFG-GP是进行语法引导的进化搜索的强大基础，并且未来的工作应集中于了解CFG-GP的参数空间以进行更好的应用。
Grammars provide a convenient and powerful mechanism to define the space of possible solutions for a range of problems. However, when used in grammatical evolution (GE), great care must be taken in the design of a grammar to ensure that the polymorphic nature of the genotype-to-phenotype mapping does not impede search. Additionally, recent work has highlighted the importance of the initialisation method on GE's performance. While recent work has shed light on the matters of initialisation and grammar design with respect to GE, their impact on other methods, such as random search and context-free grammar genetic programming (CFG-GP), is largely unknown. This paper examines GE, random search and CFG-GP under a range of benchmark problems using several different initialisation routines and grammar designs. The results suggest that CFG-GP is less sensitive to initialisation and grammar design than both GE and random search: we also demonstrate that observed cases of poor performance by CFG-GP are managed through simple adjustment of tuning parameters. We conclude that CFG-GP is a strong base from which to conduct grammar-guided evolutionary search, and that future work should focus on understanding the parameter space of CFG-GP for better application.
