Foundations of genetic programming
Foundations of genetic programming
Visualizing Tree Structures in Genetic Programming
Genetic Programming and Evolvable Machines
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Probabilistic incremental program evolution
Evolutionary Computation
Evolutionary consequences of coevolving targets
Evolutionary Computation
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis
Genetic Programming and Evolvable Machines
A cooperative coevolutionary genetic algorithm for learning bayesian network structures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Bayesian Network Approach to Program Generation
IEEE Transactions on Evolutionary Computation
An investigation of local patterns for estimation of distribution genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Graphical models and what they reveal about GP when it solves a symbolic regression problem
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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We propose graphical models as a new means of understanding genetic programming dynamics. Herein, we describe how to build an unbiased graphical model from a population of genetic programming trees. Graphical models both express information about the conditional dependency relations among a set of random variables and they support probabilistic inference regarding the likelihood of a random variable's outcome. We focus on the former information: by their structure, graphical models reveal structural dependencies between the nodes of genetic programming trees. We identify graphical model properties of potential interest in this regard - edge quantity and dependency among nodes expressed in terms of family relations. Using a simple symbolic regression problem we generate a graphical model of the population each generation. Then we interpret the graphical models with respect to conventional knowledge about the influence of subtree crossover and mutation upon tree structure.