research-article

Fault tolerant control using Cartesian genetic programming

Authors:

Yoshikazu Hirayama,

Tim Clarke,

Julian Francis MillerAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1523 - 1530

https://doi.org/10.1145/1389095.1389389

Published: 12 July 2008 Publication History

Get Access

Abstract

The paper focuses on the evolution of algorithms for control of a machine in the presence of sensor faults, using Cartesian Genetic Programming. The key challenges in creating training sets and a fitness function that encourage a general solution are discussed. The evolved algorithms are analysed and discussed. It was found that highly novel, mathematically elegant and hitherto unknown solutions were found.

References

[1]

L. Ashmore. An investigation into cartesian genetic programming within the field of art. Final year project 2000, Department of Computer Science, University of Birmingham, 2000.

Google Scholar

[2]

H. F. S. H. BÄack, T. A survey of evolution strategies. volume 1802 of Proceedings of the 4th International Conference on Genetic Algorithms, pages 2--9. Morgan Kaufmann, 1991.

Google Scholar

[3]

A. Chamseddine, H. Noura, and M. Ouladsine. Sensor fault-tolerant control for active suspension using sliding mode techniques. In Workshop on networked control systems and fault-tolerant control, Ajaccio, Corsica, France, October 2005. COSI.

Google Scholar

[4]

S. DiPaola. Evolving 'portrait painter programs' using genetic programming (Darwinian evolution) and a portrait of Darwin. website, School of Interactive Arts & Technology, Faculty of Applied Sciences, Simon Fraser University. http://www.dipaola.org/evolve/, August 2007.

Google Scholar

[5]

C. Edwards and C. Tan. Sensor fault tolerant control using sliding mode observers. Control Engineering Practice, 14:897--908, 2006.

Crossref

Google Scholar

[6]

S. Harding and J. Miller. Evolution of robot controller using cartesian genetic programming. In Proceedings of the 6th European Conference on Genetic Programming (EuroGP 2005), LNCS 3447, pages 62--72. Springer, 2005.

Digital Library

Google Scholar

[7]

T. Martinek and L. Sekanina. An evolvable image filter: experimental evaluation of a complete hardware implementation in fpga. In J. Moreno, J. Madrenas, and J. Cosp, editors, Evolvable Systems: From Biology to Hardware. Proceedings Lecture Notes in Computer Science, volume 3637 of 6th International Conference, ICES 2005, pages 76--85, Sitges, Spain, September 2005. Springer-Verlag, Berlin, Germany.

Digital Library

Google Scholar

[8]

J. Miller. An empirical study of the e±ciency of learning boolean functions using cartesian genetic programming approach. volume 2 of Proc. of GECCO, page 1135--1142. Morgan Kaufmann, 1999.

Google Scholar

[9]

J. Miller. Evolving developmental programs for adaptation, morphogenesis, and self-repair. Seventh European Conference on Artificial Life, 2801:256--265, September 2003.

Crossref

Google Scholar

[10]

J. Miller, D. Job, and V. Vassilev. Principles in the evolutionary design of digital circuits - part i. Journal of Genetic Programming and Evolvable Machines, 1(2):259--288, 2000.

Digital Library

Google Scholar

[11]

J. Miller and P. Thompson. Cartesian genetic programming. volume 1802 of Proceedings of the 3rd European Conference on Genetic Programming, pages 121--132. Springer-Verlag, 2000.

Digital Library

Google Scholar

[12]

J. F. Miller, P. Thomson, and T. C. Fogarty. Designing electronic circuits using evolutionary algorithms. arithmetic circuits: a case study. booktitle = Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, year = 1997, pages = 105--131, publisher = Wiley.

Google Scholar

[13]

K. Slany and L. Sekanina. Fitness landscape analysis and image ¯lter evolution using functional-level cgp. In M. Ebner, M. O'Neill, A. Ekart, L. Vanneschi, and A. Esparcia, editors, Proceedings Lecture Notes in Computer Science, volume 4445 of Genetic Programming. 10th European Conference, EuroGP 2007., pages 311--320, Valencia, Spain, April 2007. Springer-Verlag.

Digital Library

Google Scholar

[14]

N. Wu. Sensor fault masking of a ship propulsion system. Control Engineering Practice, 14(11):1337--45, November 2006.

Crossref

Google Scholar

Cited By

View all

Cornejo Maceda GNoack B(2023)Evolutionary Machine Learning in ControlHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_22(629-656)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_22
Miller JTurner AEsparcia-Alcázar A(2015)Cartesian Genetic ProgrammingProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2756571(179-198)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2756571
Miller JAlba E(2013)GECCO 2013 tutorialProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2464578(715-740)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2464578
Show More Cited By

Index Terms

Fault tolerant control using Cartesian genetic programming

Recommendations

Solving real-valued optimisation problems using cartesian genetic programming
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Classical Evolutionary Programming (CEP) and Fast Evolutionary Programming (FEP) have been applied to real-valued function optimisation. Both of these techniques directly evolve the real-values that are the arguments of the real-valued function. In this ...
A new crossover technique for Cartesian genetic programming
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents' trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic ...
A comparison of Cartesian genetic programming and linear genetic programming
EuroGP'08: Proceedings of the 11th European conference on Genetic programming

Two prominent genetic programming approaches are the graph-based Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP). Recently, a formal algorithm for constructing a directed acyclic graph (DAG) from a classical LGP instruction ...

Comments

Information & Contributors

Information

Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
174
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Cornejo Maceda GNoack B(2023)Evolutionary Machine Learning in ControlHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_22(629-656)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_22
Miller JTurner AEsparcia-Alcázar A(2015)Cartesian Genetic ProgrammingProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739482.2756571(179-198)Online publication date: 11-Jul-2015
https://dl.acm.org/doi/10.1145/2739482.2756571
Miller JAlba E(2013)GECCO 2013 tutorialProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2464578(715-740)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2464578
Miller JHarding SMoore J(2012)GECCO 2012 tutorialProceedings of the 14th annual conference companion on Genetic and evolutionary computation10.1145/2330784.2330932(1093-1116)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330784.2330932
Miller JHarding SLanzi P(2011)GECCO 2011 tutorialProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002136(1261-1284)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2002136
Miller JHarding SPelikan MBranke J(2010)Cartesian genetic programmingProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830924(2927-2948)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830924
Miller JHarding SRothlauf F(2009)Cartesian genetic programmingProceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers10.1145/1570256.1570428(3489-3512)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1570256.1570428
Miller JHarding SRyan C(2008)Cartesian genetic programmingProceedings of the 10th annual conference companion on Genetic and evolutionary computation10.1145/1388969.1389075(2701-2726)Online publication date: 12-Jul-2008
https://dl.acm.org/doi/10.1145/1388969.1389075

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Solving real-valued optimisation problems using cartesian genetic programming

A new crossover technique for Cartesian genetic programming

A comparison of Cartesian genetic programming and linear genetic programming

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations