research-article

How online simplification affects building blocks in genetic programming

Authors:

Mengjie ZhangAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 979 - 986

https://doi.org/10.1145/1569901.1570035

Published: 08 July 2009 Publication History

Abstract

This paper investigates the effect on building blocks during evolution of two online program simplification methods in genetic programming. The two simplification methods considered are algebraic simplification and numerical simplification. The building blocks considered are of a more general form (two and three level subtrees) than numeric constants only. Unlike most of the existing work which often uses simple symbolic regression tasks, this work considers classification tasks as examples. We develop a new method for encoding possible building blocks for the analysis. The results show that the two online program simplification methods can generate new diverse building blocks during evolution although they also destroy existing ones and that many of the existing building blocks are retained during evolution. Compared with the canonical genetic programming method, the two simplification methods can generate much smaller programs, use much shorter evolutionary training time and achieve comparable effectiveness performance.

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Cited By

Kronberger GOlivetti de França F(2024)Effects of Reducing Redundant Parameters in Parameter Optimization for Symbolic Regression using Genetic ProgrammingJournal of Symbolic Computation10.1016/j.jsc.2024.102413(102413)Online publication date: Dec-2024
https://doi.org/10.1016/j.jsc.2024.102413
Wang SMei YZhang MYao X(2022)Genetic Programming With Niching for Uncertain Capacitated Arc Routing ProblemIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.309526126:1(73-87)Online publication date: Feb-2022
https://doi.org/10.1109/TEVC.2021.3095261
Javed NGobet FLane P(2022)Simplification of genetic programs: a literature surveyData Mining and Knowledge Discovery10.1007/s10618-022-00830-736:4(1279-1300)Online publication date: 27-Apr-2022
https://doi.org/10.1007/s10618-022-00830-7
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Index Terms

How online simplification affects building blocks in genetic programming
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2009

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July 8 - 12, 2009

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Kronberger GOlivetti de França F(2024)Effects of Reducing Redundant Parameters in Parameter Optimization for Symbolic Regression using Genetic ProgrammingJournal of Symbolic Computation10.1016/j.jsc.2024.102413(102413)Online publication date: Dec-2024
https://doi.org/10.1016/j.jsc.2024.102413
Wang SMei YZhang MYao X(2022)Genetic Programming With Niching for Uncertain Capacitated Arc Routing ProblemIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.309526126:1(73-87)Online publication date: Feb-2022
https://doi.org/10.1109/TEVC.2021.3095261
Javed NGobet FLane P(2022)Simplification of genetic programs: a literature surveyData Mining and Knowledge Discovery10.1007/s10618-022-00830-736:4(1279-1300)Online publication date: 27-Apr-2022
https://doi.org/10.1007/s10618-022-00830-7
Panda SMei Y(2021)Genetic Programming with Algebraic Simplification for Dynamic Job Shop Scheduling2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9505010(1848-1855)Online publication date: 28-Jun-2021
https://doi.org/10.1109/CEC45853.2021.9505010
Chen CLuo CJiang Z(2018)Block building programming for symbolic regressionNeurocomputing10.1016/j.neucom.2017.10.047275:C(1973-1980)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.10.047
Chen CLuo CJiang Z(2018)A multilevel block building algorithm for fast modeling generalized separable systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.05.021109:C(25-34)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1016/j.eswa.2018.05.021
Haslam EXue BZhang M(2016)Further investigation on genetic programming with transfer learning for symbolic regression2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744245(3598-3605)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7744245
Worm TChiu KAlba E(2013)Prioritized grammar enumerationProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463486(1021-1028)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463486
Araseki H(2012)Effectiveness of scale-free properties in genetic programmingThe 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems10.1109/SCIS-ISIS.2012.6505204(285-289)Online publication date: Nov-2012
https://doi.org/10.1109/SCIS-ISIS.2012.6505204
Wolfson KZakov SSipper MZiv-Ukelson M(2011)Have your spaghetti and eat it tooGenetic Programming and Evolvable Machines10.1007/s10710-010-9122-112:2(121-160)Online publication date: 1-Jun-2011
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