Binary encoding for prototype tree of probabilistic model building GP
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Sampling bias in estimation of distribution algorithms for genetic programming using prototype trees
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
An adaptive knowledge evolution strategy for finding near-optimal solutions of specific problems
Expert Systems with Applications: An International Journal
An investigation of local patterns for estimation of distribution genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
Introducing graphical models to analyze genetic programming dynamics
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
International Journal of Knowledge-based and Intelligent Engineering Systems
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Genetic programming (GP) is a powerful optimization algorithm that has been applied to a variety of problems. This algorithm can, however, suffer from problems arising from the fact that a crossover, which is a main genetic operator in GP, randomly selects crossover points, and so building blocks may be destroyed by the action of this operator. In recent years, evolutionary algorithms based on probabilistic techniques have been proposed in order to overcome this problem. In the present study, we propose a new program evolution algorithm employing a Bayesian network for generating new individuals. It employs a special chromosome called the expanded parse tree , which significantly reduces the size of the conditional probability table (CPT). Prior prototype tree-based approaches have been faced with the problem of huge CPTs, which not only require significant memory resources, but also many samples in order to construct the Bayesian network. By applying the present approach to three distinct computational experiments, the effectiveness of this new approach for dealing with deceptive problems is demonstrated.