Please use this identifier to cite or link to this item:
Title: Grammar-Based Genetic Programming with Bayesian network
Authors: Wong, Pak-Kan 
Lo, Leung Yau 
Wong, Man-Leung 
Prof. LEUNG Kwong Sak 
Issue Date: 2014
Publisher: IEEE
Source: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 6900423, 2014, pp. 739-746
Journal: Proceedings of the 2014 IEEE Congress on Evolutionary Computation 
Abstract: Grammar-Based Genetic Programming (GBGP) improves the search performance of Genetic Programming (GP) by formalizing constraints and domain specific knowledge in grammar. The building blocks (i.e. the functions and the terminals) in a program can be dependent. Random crossover and mutation destroy the dependence with a high probability, hence breeding a poor program from good programs. Understanding on the syntactic and semantic in the grammar plays an important role to boost the efficiency of GP by reducing the number of poor breeding. Therefore, approaches have been proposed by introducing context sensitive ingredients encoded in probabilistic models. In this paper, we propose Grammar-Based Genetic Programming with Bayesian Network (BGBGP) which learns the dependence by attaching a Bayesian network to each derivation rule and demonstrates its effectiveness in two benchmark problems.
Type: Conference Paper
DOI: 10.1109/CEC.2014.6900423
Appears in Collections:Publication

Show full item record


checked on Dec 5, 2023

Page view(s)

checked on Dec 5, 2023

Google ScholarTM

Impact Indices




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.