Grammar-based genetic programming with dependence learning and Bayesian network classifier
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
Association for Computing Machinery
Bayesian network, Classifier, Genetic programming, Grammar-based genetic programming
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.
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Paper presented at the 16th Genetic and Evolutionary Computation Conference (GECCO), Jul 12-16, 2014, Vancouver, Canada.
ISBN of the source publication: 9781450326629
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