Grammar-based genetic programming with Bayesian network

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Book chapter

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Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

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Institute of Electrical and Electronics Engineers Inc.


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.



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Copyright © 2014 IEEE. Access to external full text or publisher's version may require subscription.

Additional Information

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

ISBN of the source publication: 9781479914883

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Recommended Citation

Wong, P.-K., L, 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). doi: 10.1109/CEC.2014.6900423