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.
翻译:语法学为确定一系列问题的可能解决办法的空间提供了一个方便而有力的机制,然而,在语法演变中使用时,必须十分小心地设计语法,以确保基因型至苯型绘图的多元形态性不妨碍搜索;此外,最近的工作突出了基因型至苯型绘图的初始化方法对基因组性能的重要性;虽然最近的工作揭示了与基因组有关的初始化和语法设计问题,但对其他方法,例如随机搜索和无背景语法基因方案(CFG-GP)的影响基本上不为人所知。本文用几种不同的初始化常规和语法设计来审查GE、随机搜索和CFG-GP的一系列基准问题。结果显示,CFG-GG对初始化和语法设计较不敏感。虽然最近的工作揭示了与GE和随机搜索相比的初始化和语法设计相比,观察到的GFG-G-G的绩效不佳案例是通过简单的调整参数加以管理的。我们的结论是,CFG-GGG-G-G是一个强有力的基础,从这一基础,从中可以进行更好的空间进制导法研究,以便更好地了解G-GR的今后的工作。