Eurofuse 2011 pp 375-387 | Cite as

Modelling Fish Habitat Preference with a Genetic Algorithm-Optimized Takagi-Sugeno Model Based on Pairwise Comparisons

  • Shinji Fukuda
  • Willem Waegeman
  • Ans Mouton
  • Bernard De Baets
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 107)


Species-environment relationships are used for evaluating the current status of target species and the potential impact of natural or anthropogenic changes of their habitat. Recent researches reported that the results are strongly affected by the quality of a data set used. The present study attempted to apply pairwise comparisons to modelling fish habitat preference with Takagi-Sugeno-type fuzzy habitat preference models (FHPMs) optimized by a genetic algorithm (GA). The model was compared with the result obtained from the FHPM optimized based on mean squared error (MSE). Three independent data sets were used for training and testing of these models. The FHPMs based on pairwise comparison produced variable habitat preference curves from 20 different initial conditions in the GA. This could be partially ascribed to the optimization process and the regulations assigned. This case study demonstrates applicability and limitations of pairwise comparison-based optimization in an FHPM. Future research should focus on a more flexible learning process to make a good use of the advantages of pairwise comparisons.


Pairwise Comparison Membership Function Mean Square Error Habitat Preference Habitat Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shinji Fukuda
    • 1
  • Willem Waegeman
    • 2
  • Ans Mouton
    • 3
  • Bernard De Baets
    • 2
  1. 1.Kyushu UniversityFukuokaJapan
  2. 2.Ghent UniversityGhentBelgium
  3. 3.Research Institute for Nature and Forest (INBO)BrusselsBelgium

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