Grammar-Based Genetic Programming with Bayesian network

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 th...

Full description

Saved in:
Bibliographic Details
Main Authors: Pak-Kan Wong, Leung-Yau Lo, Man-Leung Wong, Kwong-Sak Leung
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Biological cells
Context
Genetic algorithms
Genetic programming
Grammar
Probabilistic logic
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2014.6900423