Title

Grammar-based genetic programming with dependence learning and Bayesian network classifier

Authors

Pak Kan WONG, Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong
Leung Yau LO, Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong
Man Leung WONG, Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong KongFollow
Kwong Sak LEUNG, Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong

Document Type

Book chapter

Source Publication

GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference

Publication Date

7-12-2014

First Page

959

Last Page

966

Publisher

Association for Computing Machinery

Keywords

Bayesian network, Classifier, Genetic programming, Grammar-based genetic programming

Abstract

Grammar-Based Genetic Programming formalizes constraints on the solution structure based on domain knowledge to reduce the search space and generate grammatically correct individuals. Nevertheless, building blocks in a program can often be dependent, so the effective search space can be further reduced. Approaches have been proposed to learn the dependence using probabilistic models and shown to be useful in finding the optimal solutions with complex structure. It raises questions on how to use the individuals in the population to uncover the underlying dependence. Usually, only the good individuals are selected. To model the dependence better, we introduce Grammar-Based Genetic Programming with Bayesian Network Classifier (GBGPBC) which also uses poorer individuals. With the introduction of class labels, we further propose a refinement technique on probability distribution based on class label. Our results show that GBGPBC performs well on two benchmark problems. These techniques boost the performance of our system.

DOI

10.1145/2576768.2598256

Publisher Statement

Copyright © 2014 is held by the owner/author(s). Access to external full text or publisher's version may require subscription.

Additional Information

Paper presented at the 16th Genetic and Evolutionary Computation Conference (GECCO), Jul 12-16, 2014, Vancouver, Canada.

ISBN of the source publication: 9781450326629

Full-text Version

Publisher’s Version

Language

English

Recommended Citation

Wong, P.-K., Lo, L.-Y., Wong, M.-L., & Leung, K.-S. (2014). Grammar-based genetic programming with dependence learning and Bayesian network classifier. In C. Igel(Ed.), GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference (pp.959-966). New York: Association of Computer Machinery. doi: 10.1145/2576768.2598256