- author = "Muhammad Sarmad Ali and Meghana Kshirsagar and Enrique Naredo and Conor Ryan",
- title = "Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression",
- journal = "SN Computer Science",
- year = "2023",
- volume = "4",
- pages = "Article no: 402",
- keywords = "genetic algorithms, genetic programming, Grammatical evolution, Grammar pruning, Production ranking, Effective genome length",
-
URL = "
https://rdcu.be/evF9r",
-
DOI = "
doi:10.1007/s42979-023-01840-y",
- size = "17 pages",
- abstract = "... 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 used 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.",
-
notes = "This article is part of the topical collection
'Computational Intelligence 2021' guest edited by Juan
Julian Merelo, Kevin Warwick, Thomas Baeck, Christian
Wagner, Jonathan Garibaldi, H. K. Lam and Marie
Cottrell.
Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick V94 T9PX, Ireland",
