Grammar-based genetic programming with Bayesian network

Pak Kan WONG, Leung Yau LO, Man Leung WONG, Kwong Sak LEUNG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

5 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages739-746
Number of pages8
ISBN (Print)9781479914883
DOIs
Publication statusPublished - 22 Sep 2014

Bibliographical note

Paper presented at the IEEE Congress on Evolutionary Computation (CEC), Jul 06-11, 2014, Beijing, China.

Fingerprint Dive into the research topics of 'Grammar-based genetic programming with Bayesian network'. Together they form a unique fingerprint.

  • Projects

    Cite this

    WONG, P. K., LO, L. Y., WONG, M. L., & LEUNG, K. S. (2014). Grammar-based genetic programming with Bayesian network. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 739-746). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2014.6900423