Probabilistic Structured Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Megane:2022:CEC,

• author = "Jessica Megane and Nuno Lourenco and Penousal Machado",
• title = "Probabilistic Structured Grammatical Evolution",
• booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2022",
• editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
• address = "Padua, Italy",
• month = "18-23 " # jul,
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Sociology, Production, Benchmark testing, Syntactic, Germanium, Probabilistic logic, Search problems, Grammar-based Genetic Programming, Grammar Design, Probabilistic",
• isbn13 = "978-1-6654-6708-7",
• DOI = "doi:10.1109/CEC55065.2022.9870397",
• abstract = "The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restricting the solutions to its syntax. In this work, we propose Probabilistic Structured Grammatical Evolution (PSGE), a new approach that combines the Structured Grammatical Evolution (SGE) and Probabilistic Grammatical Evolution (PGE) representation variants and mapping mechanisms. The genotype is a set of dynamic lists, one for each non-terminal in the grammar, with each element of the list representing a probability used to select the next Probabilistic Context-Free Grammar (PCFG) derivation rule. PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied. In comparison to PGE, PSGE outperformed 4 of the 6 problems analysed.",
• notes = "Also known as \cite{9870397}",

}

Genetic Programming entries for Jessica Megane Nuno Lourenco Penousal Machado

Citations