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.

References

[1]

]]W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic Programming: An Introduction on the Automatic Evolution of computer programs and its Applications. Morgan Kaufmann Publishers. 1998.

Digital Library

[2]

]]M. Brameier and W. Banzhaf. A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation, 5(1):17--26, feb 2001.

Digital Library

[3]

]]A. Ekart. Shorter fitness preserving genetic programs. In C. Fonlupt, et al. editors, Proccedings of 4th European Conference on Artificial Evolution, volume 1829 of LNCS, pages 73--83, 2000.

Digital Library

[4]

]]M. Forina, and R. Leardi, C. Armanino, and S. Lanteri. Parvus: an extendable package of programs for data exploration, classification and correlation. Elsevier Scientific, 1988. Amsterdam, The Netherlands.

[5]

]]S. Gustafson, A. Ekart, E. Burke, and G. Kendall. Problem difficulty and code growth in genetic programming. Genetic Programming and Evolvable Machines, 5(3):271--290, Sept. 2004.

Digital Library

[6]

]]D. Hooper and N. S. Flann. Improving the accuracy and robustness of genetic programming through expression simplification. In J. R. Koza, et al. editors, Genetic Programming 1996: Proceedings of the First Annual Conference, page 428, 1996.

Digital Library

[7]

]]D. Kinzett, M. Zhang, and M. Johnston. Using numerical simplification to control bloat in genetic programming. In Proceedings of the 7th International Conference Simulated Evolution and Learning (SEAL2008), pages 493--502, 2008.

Digital Library

[8]

]]J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.

Digital Library

[9]

]]J. R. Koza, M. A. Keane, M. J. Streeter, W. Mydlowec, J. Yu, and G. Lanza. Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, 2003.

Digital Library

[10]

]]W. B. Langdon. Quadratic bloat in genetic programming. In D. Whitley, et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 451--458, 2000. Morgan Kaufmann.

[11]

]]W. B. Langdon and R. Poli. Fitness causes bloat. In P. K. Chawdhry, et al., editors, Soft Computing in Engineering Design and Manufacturing, pages 13--22. Springer-Verlag London, 23-27 June 1997.

[12]

]]S. Luke and L. Panait. Lexicographic parsimony pressure. In W. B. Langdon, et al., editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 829--836, 2002.

[13]

]]S. Luke and L. Panait. A comparison of bloat control methods for genetic programming. Evolutionary Computation, 14(3):309--344, Fall 2006.

Digital Library

[14]

]]D. Marshall. The discrete cosine transform. http://www.cs.cf.ac.uk/Dave/Multimedia/node231.htm, 2001.

[15]

]]P. Nordin and W. Banzhaf. Complexity compression and evolution. In L. Eshelman, editor, Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), pages 310--317, 1995. Morgan Kaufmann.

Digital Library

[16]

]]D. Parrott, X. Li, and V. Ciesielski. Multi-objective techniques in genetic programming for evolving classifiers. In D. Corne, et al., editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 2, pages 1141--1148, 2005.

[17]

]]R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008.

Digital Library

[18]

]]S. Silva and E. Costa. Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines. 2009. Online First.

Digital Library

[19]

]]J. F. Smith, III. Genetic program based data mining for fuzzy decision trees. In Z. R. Yang, et al. editors, Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings, pages 464--470, 2004. Springer.

[20]

]]T. Soule and J. A. Foster. Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation, 6(4):293--309, Winter 1998.

Digital Library

[21]

]]M. J. Streeter. The root causes of code growth in genetic programming. In C. Ryan, et al. editors, Genetic Programming, Proceedings of Euro GP'2003, pages 443--454, 2003. Springer-Verlag.

Digital Library

[22]

]]I. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2 edition, 2005.

Digital Library

[23]

]]P. Wong and M. Zhang. Algebraic simplification of GP programs during evolution. In M. Keijzer, et al., editors, GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 1, pages 927--934, 2006. ACM Press.

Digital Library

[24]

]]P. Wong and M. Zhang. Effects of program simplification on simple building blocks in genetic programming. In IEEE Congress on Evolutionary Computation, pages 1570--1577, 2007.

[25]

]]B.-T. Zhang and H. M¨ uhlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3(1):17--38, 1995.

Digital Library

[26]

]]M. Zhang, Y. Zhang, and W. D. Smart. Program simplification in genetic programming for object classification. In R. Khosla, et al., editors, Proceedings of the 9th International Conference Knowledge-Based Intelligent Information and Engineering Systems, Part III, pages 988--996, 2005. Springer.

Digital Library

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
Show More Cited By

Index Terms

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

Recommendations

Numerical-node building block analysis of genetic programming with simplification
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

This paper investigates the effects on building blocks of using online simplification in a GP system. Numerical nodes are tracked through individual runs to observe their behaviour. Results show that simplification disrupts building blocks early on, but ...
Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming
APCASE '15: Proceedings of the 2015 Asia-Pacific Conference on Computer Aided System Engineering

Often, the performance of genetic programming (GP) is explained in terms of building blocks--high-quality solution elements that get gradually assembled into larger and more complex patterns by the evolutionary process. However, the weak theoretical ...
Estimating the distribution and propagation of genetic programming building blocks through tree compression
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Shin et al [19] and McKay et al [15] previously applied tree compression and semantics-based simplification to study the distribution of building blocks in evolving Genetic Programming populations. However their method could only give static estimates ...

Comments

Information & Contributors

Information

Published In

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO09

Sponsor:

GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
215
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

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
https://dl.acm.org/doi/10.1007/s10710-010-9122-1
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents