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Evolutionary multiobjective optimization and multiobjective fuzzy system design

Author:

Hisao IshibuchiAuthors Info & Claims

CSTST '08: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology

Pages 3 - 4

https://doi.org/10.1145/1456223.1456226

Published: 28 October 2008 Publication History

Abstract

Evolutionary multiobjective optimization (EMO) is one of the most active research areas in evolutionary computation. EMO algorithms have been successfully used in various application areas. Among them are multiobjective design of neural networks and fuzzy systems. Especially, fuzzy system design has often been discussed as multiobjective problems. This is because we have two conflicting objectives in the design of fuzzy systems: accuracy maximization and complexity minimization. In this paper, we first explain some basic concepts in multiobjective optimization, a basic framework of EMO algorithms and some hot research issues in the EMO community. Next we explain EMO-based approaches to the design of fuzzy systems. We demonstrate through computational experiments that a large number of non-dominated fuzzy systems with different accuracy-complexity tradeoffs can be obtained by a single run of an EMO algorithm. Then we describe the use of EMO algorithms in other areas such as neural networks, genetic programming, clustering, feature selection, and data mining.

References

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Abraham, A., Jain, L. C., and Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications. Springer, Berlin (2005).

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Cococcioni, M. Evolutionary Multiobjective Optimization of Fuzzy Rule-Based Systems Bibliography Page. http://www2.ing.unipi.it:80/~o613499/emofrbss.html

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Coello, C. A. C. EMOO Web Page http://www.lania.mx/~ccoello/EMOO/

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Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001).

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Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6, 2 (2002) 182--197.

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Ghosh, A., Dehuri, K. S., and Ghosh, S. (eds.) Multi-objective Evolutionary Algorithms for Knowledge Discovery from Databases. Springer, Berlin (2008).

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Ishibuchi, H. Evolutionary multiobjective design of fuzzy rule-based systems. Proc. of 2007 IEEE Symposium on Foundation of Computational Intelligence (2007) 9--16.

[8]

Ishibuchi, H. Multiobjective genetic fuzzy systems: Review and future research directions. Proc. of 2007 IEEE International Conference on Fuzzy Systems (2007) 913--918.

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Ishibuchi, H., Murata, T., and Turksen, I. B. Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets and Systems 89, 2 (1997) 135--150.

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Ishibuchi, H., Nakashima, T., and Murata, T. Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences 136, 1--4 (2001) 109--133.

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Ishibuchi, H., and Nojima, Y. Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. International Journal of Approximate Reasoning 44, 1 (2007) 4--31.

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Ishibuchi, H., Nozaki, K., Yamamoto, N., and Tanaka, H. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. on Fuzzy Systems 3, 3 (1995) 260--270.

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Ishibuchi, H., Tsukamoto, N., Hitotsuyanagi, Y., and Nojima, Y. Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. Proc. of 2008 Genetic and Evolutionary Computation Conference (2008) 649--656.

Digital Library

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Ishibuchi, H., Tsukamoto, N., and Nojima, Y. Evolutionary many-objective optimization: A short review. Proc. of 2008 IEEE Congress on Evolutionary Computation (2008) 2424--2431.

[15]

Ishibuchi, H., and Yamamoto, T. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems 141, 1 (2004) 59--88.

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Nojima, Y., Ishibuchi, and H., Kuwajima, I. Parallel distributed genetic fuzzy rule selection. Soft Computing (in press).

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Cited By

Meyer RO'keefe S(2018)A fuzzy binary neural network for interpretable classificationsNeurocomputing10.1016/j.neucom.2013.05.030121(401-415)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2013.05.030
Wang ZPalade V(2010)Multi-objective evolutionary algorithms based Interpretable Fuzzy models for microarray gene expression data analysis2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2010.5706582(308-313)Online publication date: Dec-2010
https://doi.org/10.1109/BIBM.2010.5706582

Index Terms

Evolutionary multiobjective optimization and multiobjective fuzzy system design
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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Information & Contributors

Information

Published In

cover image ACM Other conferences

CSTST '08: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology

October 2008

733 pages

ISBN:9781605580463

DOI:10.1145/1456223

Conference Chair:
Lotfi A. Zadeh
University of California
,
General Chairs:
Youakim Badr
INSA-Lyon, France
,
Ajith Abraham
Norwegian University of Science and Technology, Norway
,
Yukio Ohsawa
Tokyo Institute of Technology, Japan
,
Program Chairs:
Richard Chbeir
University of Bourgogne, France
,
Fernando Ferri
IRPPS-CNR, Italy
,
Mario Koeppen
Kyushu Institute of Technology, Japan
,
Dominique Laurent
University of Cergy-Pontoise, France

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

The French Chapter of ACM Special Interest Group on Applied Computing
Ministère des Affaires Etrangères et Européennes
Région Ile de France
Communauté d'Agglomération de Cergy-Pontoise
Institute of Electrical and Electronics Engineers Systems, Man and Cybernetics Society
The European Society For Fuzzy And technology
Institute of Electrical and Electronics Engineers France Section
Laboratoire des Equipes Traitement des Images et du Signal
AFIHM: Ass. Francophone d'Interaction Homme-Machine
The International Fuzzy System Association
Laboratoire Innovation Développement
University of Cergy-Pontoise
The World Federation of Soft Computing
Agence de Développement Economique de Cergy-Pontoise
The European Neural Network Society
Comité d'Expansion Economique du Val d'Oise

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2008

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Cited By

Meyer RO'keefe S(2018)A fuzzy binary neural network for interpretable classificationsNeurocomputing10.1016/j.neucom.2013.05.030121(401-415)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2013.05.030
Wang ZPalade V(2010)Multi-objective evolutionary algorithms based Interpretable Fuzzy models for microarray gene expression data analysis2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2010.5706582(308-313)Online publication date: Dec-2010
https://doi.org/10.1109/BIBM.2010.5706582

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