Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
The royal tree problem, a benchmark for single and multiple population genetic programming
Advances in genetic programming
IEEE Expert: Intelligent Systems and Their Applications
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimized Substructure Discovery for Semi-structured Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Evolving Modules in Genetic Programming by Subtree Encapsulation
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Probabilistic incremental program evolution
Evolutionary Computation
A polynomial space and polynomial delay algorithm for enumeration of maximal motifs in a sequence
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
HS-Model: a hierarchical statistical subtree-generating model for genetic programming
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Symbolic regression using nearest neighbor indexing
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis
Genetic Programming and Evolvable Machines
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One crucial issue in genetic programming (GP) is how to acquire promising building blocks efficiently. In this paper, we propose a GP method (called GPTM, GP with Tree Mining) which protects the subtrees repeatedly appearing in superior individuals. Currently GPTM utilizes a FREQT-like efficient data mining method to find such subtrees. GPTM is evaluated by three benchmark problems, and the results indicate that GPTM is comparable to or better than POLE, one of the most advanced probabilistic model building GP methods, and finds the optimal individual earlier than the standard GP and POLE.