Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Discovery of subroutines in genetic programming
Advances in genetic programming
Solving the artificial ant on the Santa Fe trail problem in 20,696 fitness evaluations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Accelerating genetic programming by frequent subtree mining
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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In genetic programming with subtrees, two issues are crucial: how to acquire promising subtrees efficiently and how to keep these subtrees to be used repeatedly in the evolutional process. In this paper, we propose a hierarchical statistical model for program trees, named HS-Model, to deal with both the above issues. The HS-Model conducts statistic analysis of the current population and generates superior subtrees automatically with efficiency. The HS-Model leaves out the tedious operations to keep the promising subtrees for reusing and also omits updating the subtree library according to certain criterion. Experimental results on solving the classical artificial ant problem proved the effectiveness and the efficiency of our proposed method.