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 Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
Reliable roll force prediction in cold mill using multiple neural networks
IEEE Transactions on Neural Networks
On the architecture and implementation of tree-based genetic programming in HeuristicLab
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Specific modification of a GPA-ES evolutionary system suitable for deterministic chaos regression
Computers & Mathematics with Applications
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This work concentrates on three different modifications of a genetic programming system for symbolic regression analysis. The coefficient of correlation R2 is used as fitness function instead of the mean squared error and offspring selection is used to ensure a steady improvement of the achieved solutions. Additionally, as the fitness evaluation consumes most of the execution time, the generated solutions are only evaluated on parts of the training data to speed up the whole algorithm. These three algorithmic adaptations are incorporated in the symbolic regression algorithm and their impact is tested on two real world datasets describing a blast furnace and a temper mill process. The effect on the achieved solution quality as well as on the produced models are compared to results generated by a symbolic regression algorithm without the mentioned modifications and the benefits are highlighted.