Paper Info
Title
Learning Grammar Rules in Probabilistic Grammar-Based Genetic Programming.
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.
Year
DOI
Venue
2016
10.1007/978-3-319-49001-4_17
Lecture Notes in Computer Science
Keywords
DocType
Volume
Genetic programming,Estimation of distribution programming,Adaptive grammar,Bayesian network
Conference
10071
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
8
3
Name
Order
Citations
PageRank
Pak-Kan Wong153.85
Man-Leung Wong264451.23
Kwong-Sak Leung31887205.58