research-article

The Use of an Analytic Quotient Operator in Genetic Programming

Authors:

Ji Ni,

Russ H. Drieberg,

Peter I. RockettAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 17, Issue 1

Pages 146 - 152

https://doi.org/10.1109/TEVC.2012.2195319

Published: 01 February 2013 Publication History

Abstract

We propose replacing the division operator used in genetic programming with an analytic quotient (AQ) operator. We demonstrate that this AQ operator systematically yields lower mean squared errors over a range of regression tasks, due principally to removing the discontinuities or singularities that can often result from using either protected or unprotected division. Further, the AQ operator is differentiable. We also show that the new AQ operator stabilizes the variance of the intermediate quantities in the tree.

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Zhang HChen QXue BBanzhaf WZhang MLi XHandl J(2024)Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for RegressionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654075(998-1006)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654075
Zhang HChen QXue BBanzhaf WZhang M(2024)Modular Multitree Genetic Programming for Evolutionary Feature Construction for RegressionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.331863828:5(1455-1469)Online publication date: 1-Oct-2024
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Zhang HZhou AChen QXue BZhang M(2024)SR-Forest: A Genetic Programming-Based Heterogeneous Ensemble Learning MethodIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.324317228:5(1484-1498)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TEVC.2023.3243172
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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 17, Issue 1

February 2013

152 pages

ISSN:1089-778X

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IEEE Press

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Published: 01 February 2013

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Zhang HChen QXue BBanzhaf WZhang MLi XHandl J(2024)Bias-Variance Decomposition: An Effective Tool to Improve Generalization of Genetic Programming-based Evolutionary Feature Construction for RegressionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654075(998-1006)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654075
Zhang HChen QXue BBanzhaf WZhang M(2024)Modular Multitree Genetic Programming for Evolutionary Feature Construction for RegressionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.331863828:5(1455-1469)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TEVC.2023.3318638
Zhang HZhou AChen QXue BZhang M(2024)SR-Forest: A Genetic Programming-Based Heterogeneous Ensemble Learning MethodIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.324317228:5(1484-1498)Online publication date: 1-Oct-2024
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Geiger ASobania DRothlauf F(2024)A Comprehensive Comparison of Lexicase-Based Selection Methods for Symbolic Regression ProblemsGenetic Programming10.1007/978-3-031-56957-9_12(192-208)Online publication date: 3-Apr-2024
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Moscato PCiezak ANoman NSilva SPaquete L(2023)Dynamic Depth for Better Generalization in Continued Fraction RegressionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590461(520-528)Online publication date: 15-Jul-2023
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Geiger ASobania DRothlauf FSilva SPaquete L(2023)Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression ProblemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590400(1109-1117)Online publication date: 15-Jul-2023
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Zhang HChen QXue BBanzhaf WZhang MSilva SPaquete L(2023)A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for RegressionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590365(1194-1202)Online publication date: 15-Jul-2023
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Raymond CChen QXue BZhang MSilva SPaquete L(2023)Fast and Efficient Local-Search for Genetic Programming Based Loss Function LearningProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590361(1184-1193)Online publication date: 15-Jul-2023
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