Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
Learning computer programs with the bayesian optimization algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Probabilistic incremental program evolution
Evolutionary Computation
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Bayesian automatic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
A Bayesian Network Approach to Program Generation
IEEE Transactions on Evolutionary Computation
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In recent years, program evolution algorithms based on the estimation of distribution algorithm (EDA) have been proposed to improve search ability of genetic programming (GP) and to overcome GP-hard problems. One such method is the probabilistic prototype tree (PPT) based algorithm. The PPT based method explores the optimal tree structure by using the full tree whose number of child nodes is maximum among possible trees. This algorithm, however, suffers from problems arising from function nodes having different number of child nodes. These function nodes cause intron nodes, which do not affect the fitness function. Moreover, the function nodes having many child nodes increase the search space and the number of samples necessary for properly constructing the probabilistic model. In order to solve this problem, we propose binary encoding for PPT. Here, we convert each function node to a subtree of binary nodes where the converted tree is correct in grammar. Our method reduces ineffectual search space, and the binary encoded tree is able to express the same tree structures as the original method. The effectiveness of the proposed method is demonstrated through the use of two computational experiments.