Introduction to 20 Years of Grammatical Evolution

Ryan, Conor; O’Neill, Michael; Collins, John J.

doi:10.1007/978-3-319-78717-6_1

Cited by 17 publications

(9 citation statements)

References 45 publications

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“…The combination of fragments and the evaluation of the resulting SARS-CoV-2-M pro inhibitor candidate was performed using an evolutive grammar (EG) algorithm. 36,45 The EG algorithm is a methodology using an indirect representation in the framework of Genetic Programming (GP). 37 For this reason, the EG population is formed by linear genotypes, which are transformed to their respective phenotype using a mapping process.…”

Section: Methodsmentioning

confidence: 99%

Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors

Frausto-Parada¹,

Várgas-Rodríguez²,

Marcado-Sánchez³

et al. 2022

Phys. Chem. Chem. Phys.

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Section: Methodsmentioning

confidence: 99%

Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors

Frausto-Parada¹,

Várgas-Rodríguez²,

Marcado-Sánchez³

et al. 2022

Phys. Chem. Chem. Phys.

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“…GE has been extensively used to evolve expressions by randomly combining building blocks (mathematical operators, functions, constants, and variables) represented in the grammar. A good overview can be found in the text of Ryan et al [ 2 ]. Although several works considered standard GE, there are notable efforts for using enhanced GE methods for symbolic regression, for example, Structured GE [ 15 ], Geometric Semantic GE [ 16 ], -GE [ 17 ], Hierarchical and Weighted Hierarchical GE [ 18 , 19 ].…”

Section: Related Workmentioning

confidence: 99%

“…Grammatical Evolution (GE) is a grammar-based Genetic Programming (GP) approach which has found wide acceptance in the research communities [ 1 , 2 ]. It is a bio-inspired population-based methodology from the domain of evolutionary computation which heavily relies on the definition of context-free grammars (CFGs).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression

et al. 2023

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Grammar is a key input in grammar-based genetic programming. Grammar design not only influences performance, but also program size. However, grammar design and the choice of productions often require expert input as no automatic approach exists. This research work discusses our approach to automatically reduce a bloated grammar. By utilizing a simple Production Ranking mechanism, we identify productions which are less useful and dynamically prune those to channel evolutionary search towards better (smaller) solutions. Our objective in this work was program size reduction without compromising generalization performance. We tested our approach on 13 standard symbolic regression datasets with Grammatical Evolution. Using a grammar embodying a well-defined function set as a baseline, we compare effective genome length and test performance with our approach. Dynamic grammar pruning achieved significantly better genome lengths for all datasets, while significantly improving generalization performance on three datasets, although it worsened in five datasets. When we utilized linear scaling during the production ranking stages (the first 20 generations) the results dramatically improved. Not only were the programs smaller in all datasets, but generalization scores were also significantly better than the baseline in 6 out of 13 datasets, and comparable in the rest. When the baseline was also linearly scaled as well, the program size was still smaller with the Production Ranking approach, while generalization scores dropped in only three datasets without any significant compromise in the rest.

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“…Grammars provide a convenient and powerful mechanism to define the space of possible solutions for a range of problems, and the incorporation of grammars into evolutionary computation has a history spanning over three decades [3,16,23]. While a number of methods exist for grammar-guided evolutionary computation (GGEC), most research into the use of grammars in evolution has focused upon grammatical evolution (GE), which recently marked twenty years of research and development [22]. While GE has been applied to a range of problems from regression to evolutionary design and hyperheuristics, a significant portion of GE research has focused on understanding the interaction between its linear representation, genotype-to-phenotype mapping, and the polymorphic treatment of codons within its mapping process.…”

Section: Introductionmentioning

confidence: 99%

Initialisation and Grammar Design in Grammar-Guided Evolutionary Computation

Dick¹,

Whigham²

2022

Preprint

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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. CCS CONCEPTS• Computing methodologies → Genetic programming; Supervised learning by regression.

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Introduction to 20 Years of Grammatical Evolution

Cited by 17 publications

References 45 publications

Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors

Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors

Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression

Initialisation and Grammar Design in Grammar-Guided Evolutionary Computation

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