Learning Grammar Rules in Probabilistic Grammar-Based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/tpnc/WongWL16,

• author = "Pak-Kan Wong and Man Leung Wong and Kwong-Sak Leung",
• title = "Learning Grammar Rules in Probabilistic Grammar-Based Genetic Programming",
• booktitle = "Theory and Practice of Natural Computing - 5th International Conference, {TPNC} 2016, Sendai, Japan, December 12-13, 2016, Proceedings",
• editor = "Carlos Martin-Vide and Takaaki Mizuki and Miguel A. Vega-Rodriguez",
• year = "2016",
• volume = "10071",
• isbn13 = "978-3-319-49000-7",
• pages = "208--220",
• series = "Lecture Notes in Computer Science",
• keywords = "genetic algorithms, genetic programming, estimation of distribution programming adaptive grammar Bayesian network",
• bibdate = "2017-05-21",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/tpnc/tpnc2016.html#WongWL16",
• DOI = "doi:10.1007/978-3-319-49001-4_17",
• abstract = "Grammar-based Genetic Programming (GBGP) searches for a computer program in order to solve a given problem. Grammar constrains the set of possible programs in the search space. It is not obvious to write an appropriate grammar for a complex problem. Our proposed Bayesian Grammar-Based Genetic Programming with Hierarchical Learning (BGBGP-HL) aims at automatically designing new rules from existing relatively simple grammar rules during evolution to improve the grammar structure. The new grammar rules also reflects the new understanding of the existing grammar under the given fitness evaluation function. Based on our case study in asymmetric royal tree problem, our evaluation shows that BGBGP-HL achieves the best performance among the competitors. Compared to other algorithms, search performance of BGBGP-HL is demonstrated to be more robust against dependencies and the changes in complexity of programs.",

}

Genetic Programming entries for Pak-Kan Wong Man Leung Wong Kwong-Sak Leung

Citations