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A Potential of Evolutionary Rule-based Machine Learning for Real World Applications

Published: 11 July 2015 Publication History

Abstract

This paper explores a potential of Evolutionary Rule-based Machine Learning (ERML) by showing how ERML succeeds in real world applications. Generally, ERML is defined as a method that integrates (rule-based) machine learning with evolutionary computation, where the former method contributes to a local search while the latter method contributes to a global search. From such an integrated feature of ERML, one of the fundamental interests in ERML is how to control interactions between learning and evolution to produce a performance that cannot be achieved by either of these methods alone.

References

[1]
Harada, T., Otani, M., Ichikawa, Y., Hattori, K., Sato, H., and Takadama, K.: "Robustness to Bit Inversion in Registers and Acceleration of Program Evolution in On-Board Computer," Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol. 15, No. 8, pp. 1175--1185, 2011.
[2]
Kitagawa, H., Sato, K., and Takadama, K.: "Multiagent-based Sustainable Bus Route Optimization in Disaster" Journal of Information Processing, Vol. 22, No. 2, pp. 235--242, 2014.
[3]
Takadama, K., Terano, T., Shimohara, K., Hori, K., and Nakasuka, S.: "Making Organizational Learning Operational: Implication from Learning Classifier System", Computational and Mathematical Organization Theory (CMOT), Kluwer Academic Publishers, Vol. 5, No. 3, pp. 229--252, 1999.
[4]
Takadama, K. and Nakata, M.: "Extracting Both Generalized and Specialized Knowledge by XCS using Attribute Tracking and Feedback," 2015 IEEE Congress on Evolutionary Computation (CEC2015), 2015, to appear.

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  1. A Potential of Evolutionary Rule-based Machine Learning for Real World Applications

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        cover image ACM Conferences
        GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
        July 2015
        1568 pages
        ISBN:9781450334884
        DOI:10.1145/2739482
        Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Published: 11 July 2015

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        Author Tags

        1. application
        2. evolutionary rule-based machine learning
        3. learning classifier system

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