Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural Computation
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Symbolic Regression via Genetic Programming
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary computation and structural design: A survey of the state-of-the-art
Computers and Structures
On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Incorporating expert knowledge in evolutionary search: a study of seeding methods
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Discrete dynamical genetic programming in XCS
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving iterated functions using genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Predicting solution rank to improve performance
Proceedings of the 12th annual conference on Genetic and evolutionary computation
RGP: an open source genetic programming system for the R environment
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Analytic solutions to differential equations under graph-based genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Symbolic regression of multiple-time-scale dynamical systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Dynamical genetic programming in xcsf
Evolutionary Computation
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In this paper, we analyze two general-purpose encoding types, trees and graphs systematically, focusing on trends over increasingly complex problems. Tree and graph encodings are similar in application but offer distinct advantages and disadvantages in genetic programming. We describe two implementations and discuss their evolvability. We then compare performance using symbolic regression on hundreds of random nonlinear target functions of both 1-dimensional and 8-dimensional cases. Results show the graph encoding has less bias for bloating solutions but is slower to converge and deleterious crossovers are more frequent. The graph encoding however is found to have computational benefits, suggesting it to be an advantageous trade-off between regression performance and computational effort.