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Application of Self-adapting Genetic Algorithms to Generate Fuzzy Systems for a Regression Problem

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Book cover Computational Collective Intelligence. Technologies and Applications (ICCCI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8733))

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Abstract

Six variants of self-adapting genetic algorithms with varying mutation, crossover, and selection were developed. To implement self-adaptation the main part of a chromosome which comprised the solution was extended to include mutation rates, crossover rates, and/or tournament size. The solution part comprised the representation of a fuzzy system and was real-coded whereas to implement the proposed self-adapting mechanisms binary coding was employed. The resulting self-adaptive genetic fuzzy systems were evaluated using real-world datasets derived from a cadastral system and included records referring to residential premises transactions. They were also compared in respect of prediction accuracy with genetic fuzzy systems optimized by a classical genetic algorithm, multilayer perceptron and radial basis function neural network. The analysis of the results was performed using statistical methodology including nonparametric tests followed by post-hoc procedures designed especially for multiple N×N comparisons.

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Author information

Authors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Tadeusz Lasota

  2. Dept. of Spatial Management, Wrocław University of Environmental and Life Sciences, ul. Norwida 25/27, 50-375, Wrocław, Poland

    Magdalena Smętek, Zbigniew Telec & Bogdan Trawiński

  3. Faculty of Electronics, Wrocław University of Technology, Wybrzeże S. Wyspiańskiego 27, 50-370, Wrocław, Poland

    Grzegorz Trawiński

Authors
  1. Tadeusz Lasota
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  2. Magdalena Smętek
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  3. Zbigniew Telec
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  4. Bogdan Trawiński
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  5. Grzegorz Trawiński
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Editors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Korea

    Dosam Hwang  & Jason J. Jung  & 

  2. Institute of Informatics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc-Thanh Nguyen

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Lasota, T., Smętek, M., Telec, Z., Trawiński, B., Trawiński, G. (2014). Application of Self-adapting Genetic Algorithms to Generate Fuzzy Systems for a Regression Problem. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-11289-3_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11288-6

  • Online ISBN: 978-3-319-11289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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