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Hybrid Self-configuring Evolutionary Algorithm for Automated Design of Fuzzy Classifier

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Advances in Swarm Intelligence (ICSI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8794))

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Abstract

For a fuzzy classifier automated design the hybrid self-configuring evolutionary algorithm is proposed. The self-configuring genetic programming algorithm is suggested for the choice of effective fuzzy rule bases. For the tuning of linguistic variables the self-configuring genetic algorithm is used. An additional feature of the proposed approach allows the use of genetic programming for the selection of the most informative combination of problem inputs. The usefulness of the proposed algorithm is demonstrated on benchmark tests and real world problems.

Research is fulfilled with the support of the Ministry of Education and Science of Russian Federation within State assignment project 140/14.

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Author information

Authors and Affiliations

  1. Siberian Aerospace University, Krasnoyarsky rabochy avenue 31, 660014, Krasnoyarsk, Russia

    Maria Semenkina & Eugene Semenkin

Authors
  1. Maria Semenkina
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  2. Eugene Semenkin
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Computer Science Department, CINVESTAV-IPN, Mexico

    Carlos A. Coello Coello

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© 2014 Springer International Publishing Switzerland

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Semenkina, M., Semenkin, E. (2014). Hybrid Self-configuring Evolutionary Algorithm for Automated Design of Fuzzy Classifier. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_35

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  • DOI: https://doi.org/10.1007/978-3-319-11857-4_35

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11856-7

  • Online ISBN: 978-3-319-11857-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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