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

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

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.

Keywords

  • self-adaptive GA
  • mutation
  • crossover
  • genetic fuzzy systems

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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

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