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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 language | English |
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Title of host publication | Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 739-746 |
Number of pages | 8 |
ISBN (Print) | 9781479914883 |
DOIs | |
Publication status | Published - 22 Sept 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
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Adaptive Grammar-Based Genetic Programming with Dependence Learning (基於文法及依存關係學習的適應性遺傳編程法)
WONG, M. L. (PI) & LEUNG, K. S. (CoI)
Research Grants Council (HKSAR)
1/01/12 → 30/06/15
Project: Grant Research