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Size Fair and Homologous Tree Crossovers for Tree Genetic Programming

Author:

W. B. LangdonAuthors Info & Claims

Genetic Programming and Evolvable Machines, Volume 1, Issue 1-2

Pages 95 - 119

https://doi.org/10.1023/A:1010024515191

Published: 01 April 2000 Publication History

Abstract

Size fair and homologous crossover genetic operators for tree based genetic programming are described and tested. Both produce considerably reduced increases in program size (i.e., less bloat) and no detrimental effect on GP performance.

GP search spaces are partitioned by the ridge in the number of program v. their size and depth. While search efficiency is little effected by initial conditions, these do strongly influence which half of the search space is searched. However a ramped uniform random initialization is described which straddles the ridge.

With subtree crossover trees increase about one level per generation leading to subquadratic bloat in program length.

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Published In

cover image Genetic Programming and Evolvable Machines

Genetic Programming and Evolvable Machines Volume 1, Issue 1-2

April 2000

167 pages

ISSN:1389-2576

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Copyright © Copyright © 2000 Kluwer Academic Publishers.

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Kluwer Academic Publishers

United States

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Published: 01 April 2000

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Cited By

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https://dl.acm.org/doi/10.1145/3583131.3590365
de França F(2023)Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimizationGenetic Programming and Evolvable Machines10.1007/s10710-023-09461-324:2Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1007/s10710-023-09461-3
Langdon W(2022)Deep Genetic Programming Trees Are RobustACM Transactions on Evolutionary Learning and Optimization10.1145/35397382:2(1-34)Online publication date: 16-Aug-2022
https://dl.acm.org/doi/10.1145/3539738
Mei YChen QLensen AXue BZhang M(2022)Explainable Artificial Intelligence by Genetic Programming: A SurveyIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.322550927:3(621-641)Online publication date: 28-Nov-2022
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Kuroda MHanada YOno K(2018)Effectiveness of Multi-step Crossover in Extrapolation Domain for Genetic Programming2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2018.00618(3654-3659)Online publication date: 7-Oct-2018
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