tutorial

The subsumption mechanism for XCS using code fragmented conditions

Authors:

Tzu-Chien HsiaoAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 1275 - 1282

https://doi.org/10.1145/2464576.2482706

Published: 06 July 2013 Publication History

Abstract

By utilizing a code-fragmented representation of Extended Classifier System (XCS) condition in conjunction with building-block extraction technique, autonomous scaling has been realized in the latest work of XCS. The technique substantially reduces the number of training instances required in various benchmark problems. However, the subsumption mechanism was not included in the former report of the technique. Therefore, we invented the subsumption mechanism for XCS with such technique, and observed the characteristics of such the system in multiplexer problems. The finding indicates that our subsumption mechanism decreased the number of macro-classifiers.

References

[1]

Wilson, S. W. Classifier fitness based on accuracy. Evol. Comput., 3, 2 1995, 149--175.

Digital Library

[2]

Akbar, M. A. and Farooq, M. Application of evolutionary algorithms in detection of SIP based flooding attacks. In Proceedings of the Proceedings of the 11th Annual conference on Genetic and evolutionary computation (Montreal, Canada, 2009). ACM.

Digital Library

[3]

Gandhe, A., Yu, S.-H., Mehra, R. and Smith, R. XCS for Fusing Multi-Spectral Data in Automatic Target Recognition. Springer Berlin / Heidelberg, 2008.

[4]

Armano, G., Murru, A. and Roli, F. Stock Market Prediction by a Mixture of Genetic-Neural Experts. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 16, 5 2002, 501--526.

[5]

Chen, A.-P., Hsu, Y.-C. and Chang, J.-H. Applying Extensible Classifier System to Inter-market Arbitrage with High-Frequency Financial Data. In Proceedings of the Proceedings of the 2007 International Conference on Convergence Information Technology (2007). IEEE Computer Society.

Digital Library

[6]

Tsai, W.-C. and Chen, A.-P. Global Asset Allocation Using XCS Experts in Country-Specific ETFs. 2008.

Digital Library

[7]

Passaro, A., Baronti, F. and Maggini, V. Exploring relationships between genotype and oral cancer development through XCS. In Proceedings of the Proceedings of the 2005 workshops on Genetic and evolutionary computation (Washington, D.C., 2005). ACM.

Digital Library

[8]

Baronti, F., Micheli, A., Passaro, A. and Starita, A. Machine learning contribution to solve prognostic medical. Elsevier, 2007.

[9]

Bernauer, A., Arndt, G., Bringmann, O. and Rosenstiel, W. Autonomous multi-processor-SoC optimization with distributed learning classifier systems XCS. In Proceedings of the Proceedings of the 8th ACM international conference on Autonomic computing (Karlsruhe, Germany, 2011). ACM.

Digital Library

[10]

Shankar, A. and Louis, S. Learning classifier systems for user context learning.2005.

[11]

Shankar, A. and Louis, S. J. XCS for Personalizing Desktop Interfaces. Evolutionary Computation, IEEE Transactions on, 14, 4 2010, 547--560.

Digital Library

[12]

Orriols-Puig, A., Casillas, J. and Bernadó-Mansilla, E. Genetic-based machine learning systems are competitive for pattern recognition. Evolutionary Intelligence, 1, 3 2008, 209--232.

[13]

Wilson, S. W. Get real! XCS with continuous-valued inputs. Learning Classifier Systems 2000, 209--219.

Digital Library

[14]

Lanzi, P. L. Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding. In Proceedings of the Proceedings of the genetic and evolutionary computation conference (1999).

[15]

Lanzi, P. L. and Perrucci, A. Extending the Representation of Classifier Conditions Part II: from messy coding to S-Expressions. In Proceedings of the Proceedings of the genetic and evolutionary computation conference (1999).

[16]

Lanzi, P. L. XCS with Stack-Based Genetic Programming, 2003.

[17]

Iqbal, M., Browne, W. N. and Zhang, M. Extracting and using building blocks of knowledge in learning classifier systems. In Proceedings of the Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference (Philadelphia, July 7-11, 2012). ACM.

Digital Library

[18]

Koza, J. Genetic programming as a means for programming computers by natural selection. Stat Comput, 4, 2 (1994/06/01 1994), 87--112.

[19]

Wilson, S. W. Mining oblique data with XCS. Advances in Learning Classifier Systems. Third International Workshop, IWLCS 2000. Revised Papers (Lecture Notes in Artificial Intelligence Vol. 1996 (2001), 158--174.

Digital Library

[20]

Butz, M. and Wilson, S. An Algorithmic Description of XCS. Springer Berlin / Heidelberg, 2001.

Cited By

Chen LLee PHsiao T(2015)A sensor tagging approach for reusing building blocks of knowledge in learning classifier systems2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257256(2953-2960)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7257256

Index Terms

The subsumption mechanism for XCS using code fragmented conditions

Recommendations

Extracting and using building blocks of knowledge in learning classifier systems
GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Human beings have the ability to apply the domain knowledge learned from a smaller problem to more complex problems of the same or a related domain, but currently evolutionary computation techniques lack this ability. Hence these techniques relearn from ...
Evolving optimum populations with XCS classifier systems

The main goal of the research direction is to extract building blocks of knowledge from a problem domain. Once extracted successfully, these building blocks are to be used in learning more complex problems of the domain, in an effort to produce a ...
Comparison of two methods for computing action values in XCS with code-fragment actions
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

XCS is a learning classifier system that uses accuracy-based fitness to learn a problem. Commonly, a classifier rule in XCS is encoded using a ternary alphabet based condition and a numeric action. Previously, we implemented a code-fragment action based ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 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

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial

Conference

GECCO '13

Sponsor:

SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
90
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen LLee PHsiao T(2015)A sensor tagging approach for reusing building blocks of knowledge in learning classifier systems2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257256(2953-2960)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7257256

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.

Figures

Tables

Media

View Table of Conten