research-article

Self-adaptive mutation in XCSF

Authors:

Martin V. Butz,

Patrick Stalph,

Pier Luca LanziAuthors Info & Claims

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

Pages 1365 - 1372

https://doi.org/10.1145/1389095.1389361

Published: 12 July 2008 Publication History

Abstract

Recent advances in XCS technology have shown that self-adaptive mutation can be highly useful to speed-up the evolutionary progress in XCS. Moreover, recent publications have shown that XCS can also be successfully applied to challenging real-valued domains including datamining, function approximation, and clustering. In this paper, we combine these two advances and investigate self-adaptive mutation in the XCS system for function approximation with hyperellipsoidal condition structures, referred to as XCSF in this paper. It has been shown that XCSF solves function approximation problems with an accuracy, noise robustness, and generalization capability comparable to other statistical machine learning techniques and that XCSF outperforms simple clustering techniques to which linear approximations are added. This paper shows that the right type of self-adaptive mutation can further improve XCSF's performance solving problems more parameter independent and more reliably. We analyze various types of self-adaptive mutation and show that XCSF with self-adaptive mutation ranges,differentiated for the separate classifier condition values, yields most robust performance results. Future work may further investigate the properties of the self-adaptive values and may integrate advanced self-adaptation techniques.

References

[1]

T. Bäck and H.-P. Schwefel. Evolution strategies I: Variants and their computational implementation. In G. Winter, J. Périaux, M. Gal'an, and P. Cuesta, editors, Genetic algorithms in engineering and computer science, chapter 7, pages 111--126. John Wiley & Sons, Chichester, 1995.

[2]

L. B. Booker, D. E. Goldberg, and J. H. Holland. Classifier systems and genetic algorithms. Artificial Intelligence, 40:235--282, 1989.

Digital Library

[3]

L. Bull. On lookahead and latent learning in simple lcs. In J. Bacardit, E. Bernadó-Mansilla, and M. V. Butz, editors, Learning Classifier Systems. International Workshops, IWLCS 2006-2007. Springer-Verlag, Berlin Heidelberg, 2008.

Digital Library

[4]

L. Bull, J. Hurst, and A. Tomlinson. Self-adaptive mutation in classifier system controllers. From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior, pages 460--467, 2000.

[5]

M. V. Butz. Anticipatory learning classifier systems. Kluwer Academic Publishers, Boston, MA, 2002.

[6]

M. V. Butz. Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system. GECCO 2005: Genetic and Evolutionary Computation Conference, pages 1835--1842, 2005.

Digital Library

[7]

M. V. Butz, T. Kovacs, P. L. Lanzi, and S.W.Wilson. Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation, 8:28--46, 2004.

Digital Library

[8]

M. V. Butz, P. L. Lanzi, and S. W. Wilson. Hyper-ellipsoidal conditions in XCS: Rotation, linear approximation, and solution structure. GECCO 2006: Genetic and Evolutionary Computation Conference, pages 1457--1464, 2006.

Digital Library

[9]

M. V. Butz, M. Pelikan, X. Llorà, and D. E. Goldberg. Automated global structure extraction for effective local building block processing in XCS. Evolutionary Computation, 14:345--380, 2006.

Digital Library

[10]

N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9:159--195, 2001.

Digital Library

[11]

J. H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. A. Waterman and F. Hayes-Roth, editors, Pattern directed inference systems, pages 313--329. Academic Press, New York, 1978.

[12]

J. Hurst and L. Bull. A self-adaptive XCS. In P. L. Lanzi, W. Stolzmann, and S. W. Wilson, editors, Advances in learning classifier systems: Fourth international workshop, IWLCS 2001 (LNAI 2321), pages 57--73. Springer-Verlag, Berlin Heidelberg, 2002.

Digital Library

[13]

J. Hurst and L. Bull. A neural learning classifier system with self-adaptive constructivism for mobile robot learning. Artificial Life, 12:1--28, 2006.

Digital Library

[14]

C. Igel, N. Hansen, and S. Roth. Covariance matrix adaptation for multi-objective optimization. Evolutionary Computation, 15:1--28, 2007.

Digital Library

[15]

S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149--175, 1995.

Digital Library

[16]

S. W. Wilson. Function approximation with a classifier system. Proceedings of the Third Genetic and Evolutionary Computation Conference (GECCO-2001), pages 974--981, 2001.

[17]

S. W. Wilson. Classifiers that approximate functions. Natural Computing, 1:211--234, 2002.

Digital Library

Cited By

Hansmeier TKaufmann PPlatzner MCoello Coello C(2020)An adaption mechanism for the error threshold of XCSFProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398106(1756-1764)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3398106
Franco MKrasnogor NBacardit J(2020)Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure IdentificationIEEE Computational Intelligence Magazine10.1109/MCI.2020.299823215:3(28-46)Online publication date: Aug-2020
https://doi.org/10.1109/MCI.2020.2998232
Li XHe WHirasawa K(2014)Adaptive Genetic Network Programming2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900290(1808-1815)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900290
Show More Cited By

Index Terms

Self-adaptive mutation in XCSF

Recommendations

Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and the impact on computational complexity

Michigan-style learning classifier systems iteratively evolve a distributed solution to a problem in the form of potentially overlapping subsolutions. Each problem niche is covered by subsolutions that are represented by a set of predictive rules, ...
The ($$1+\lambda $$1+ý) Evolutionary Algorithm with Self-Adjusting Mutation Rate

We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and ...
Extending XCSF beyond linear approximation
GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

XCSF is the extension of XCS in which classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. XCSF can exploit classifiers' computable prediction to evolve accurate piecewise ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

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

Copyright © 2008 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: 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

6
Total Citations
View Citations
174
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hansmeier TKaufmann PPlatzner MCoello Coello C(2020)An adaption mechanism for the error threshold of XCSFProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398106(1756-1764)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3398106
Franco MKrasnogor NBacardit J(2020)Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure IdentificationIEEE Computational Intelligence Magazine10.1109/MCI.2020.299823215:3(28-46)Online publication date: Aug-2020
https://doi.org/10.1109/MCI.2020.2998232
Li XHe WHirasawa K(2014)Adaptive Genetic Network Programming2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900290(1808-1815)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900290
Franco MKrasnogor NBacardit J(2013)GAssist vs. BioHELSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-013-1016-817:6(953-981)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1007/s00500-013-1016-8
Stalph PButz MMoore J(2012)Guided evolution in XCSFProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330289(911-918)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330289
Kovacs T(2012)Genetics-Based Machine LearningHandbook of Natural Computing10.1007/978-3-540-92910-9_30(937-986)Online publication date: 2012
https://doi.org/10.1007/978-3-540-92910-9_30

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