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

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

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

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages959-966
Number of pages8
ISBN (Print)9781450326629
DOIs
Publication statusPublished - 12 Jul 2014

Bibliographical note

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

Keywords

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

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    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 GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference (pp. 959-966). Association for Computing Machinery. https://doi.org/10.1145/2576768.2598256