Initialisation and Grammar Design in Grammar-Guided Evolutionary Computation

• URL: http://arxiv.org/abs/2204.07410v1
• Date: Fri, 15 Apr 2022 10:15:40 GMT
• Title: Initialisation and Grammar Design in Grammar-Guided Evolutionary Computation
• Authors: Grant Dick and Peter A. Whigham
• Abstract summary: We show that genetic programming (CFG-GP) is less sensitive to initialisation and grammar design than random search and GE. We also demonstrate that observed cases of poor performance by CFG-GP are managed through simple adjustment of tuning parameters.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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